Journal title Journal of Applied Data Sciences Initials JADS Abbreviation J. Appl. Data Sci. Online ISSN 2723-6471 Frequency 4 issues per year DOI doi.org/10.47738/jads Editor-in-chief Prof. Dr. Ibrahiem M. M. El Emary, (ID Scopus: 15135499400), King Abdulaziz University, Kingdom of Saudi Arabia Publisher Bright Publisher Citation Analysis Scopus | Web of Science | Google Scholar Established in 2020, the Journal of Applied Data Science (JADS) aims to advance and promote data science methods, computing, and applications in all scientific fields where knowledge and insights are to be extracted from data. The journal publishes research works on the full spectrum of the data science including statistics, computer science, and domain applications. The topics can be about any aspect of the life cycle (collecting, processing, analyzing, communicating, etc.) of data science projects from any field that involves understanding and making effective use of data. The emphasis is on applications, case studies, statistical methods, computational tools, and reviews of data science. Finally, accepted and published papers will be freely accessed in this website and the following abstracting & indexing databases: Scopus Scimago Dimensions Google Scholar Index Copernicus GARUDA: Digital Reference Garba by Ristekdikti - Indonesia Indonesia OneSearch Crossref Search ROAD: the Directory of Open Access scholarly Resources OAI Address Journal of Applied Data Sciences has OAI address: https://bright-journal.org/Journal/index.php/JADS/oai Before submission, You have to make sure that your paper is prepared using the JADS paper Template, has been carefully proofread and polished, and conformed to the author guidelines. Online Submissions Already have a Username/Password for JADS? GO TO LOGIN Need a Username/Password? GO TO REGISTRATION Registration and login are required to submit items online and to check the status of current submissions. 7 days First Time Decision 93 days Review Time 100 days Submission to Accepted 32 days Acceptance to Publication
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A Modified Watershed Algorithm for Rice Plant Growth Stage Analysis
Information technology plays a crucial role in enhancing various sectors, including agriculture. In particular, technological advancements in crop monitoring are essential for sustainable food production, where accurate growth analysis is vital. Image-based approaches have emerged as a promising tool for assessing crop growth, particularly in rice plants. This study aims to enhance rice plant image segmentation using an improved Watershed algorithm, integrating the Laplacian operator and Distance Transform. This study utilizes a Support Vector Machine (SVM) classifier for segmenting and classifying rice plant growth stages, achieving accuracy, precision, recall, and F1-score metrics. The dataset consists of 1080 images of rice plants, with 74 images used for training, 31 for testing, and 975 images for validation. The image processing pipeline involves preprocessing steps such as grayscale conversion, normalization, color segmentation, Otsu thresholding, filtering, and edge detection. Following preprocessing, the Watershed algorithm is applied in two scenarios: the conventional method and the enhanced method with the Laplacian operator and Distance Transform. Performance evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that the enhanced Watershed algorithm significantly outperforms the conventional method, achieving an accuracy of 99.58%, precision of 80.55%, recall of 79.92%, and an F1-score of 81.50%. While there is a slight imbalance in precision and recall, the model demonstrates reliable performance in identifying rice plant growth. This study confirms that integrating the Laplacian operator and Distance Transform into the Watershed algorithm significantly improves segmentation accuracy, supporting the development of automated monitoring systems in smart farming. Furthermore, this approach opens avenues for application in other crops and diverse environmental conditions.
Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming
Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.
A Hybrid TF-IDF and Knowledge Graph-Enhanced Retrieval-Augmented Generation Framework with Large Language Models for Domain-Aware Question Answering
This study aims to develop a domain-aware legal Question-Answering (QA) system tailored for Indonesia’s Micro, Small, and Medium Enterprises (MSMEs) by proposing a hybrid Retrieval-Augmented Generation (RAG) framework that integrates Term Frequency–Inverse Document Frequency (TF-IDF), Knowledge Graph (KG), and Large Language Model (LLM) components. In this framework, TF-IDF contributes by performing lexical-level retrieval to identify the most relevant documents based on keyword weighting; the KG enriches this retrieval by providing semantic relationships among legal entities, enabling deeper contextual understanding; and the LLM generates coherent responses conditioned on both lexical and semantically grounded evidence. Together, these components work synergistically to strengthen factual grounding during retrieval and improve contextual reasoning during generation. Methodologically, the system processes a curated dataset of 1,400 legal question–answer pairs collected from national legal repositories, including legislation, government regulations, and MSME digitalization guidelines. The process includes text preprocessing, keyword extraction using TF-IDF, semantic enrichment through a KG that maps legal entities and their relationships, and answer generation via an LLM powered by the RAG pipeline. The system was evaluated using Precision, Recall, F1-Score, Bilingual Evaluation Understudy (BLEU), and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, validated by five legal experts. Results show an accuracy improvement from 76.5% to 83.5% after integrating KG, with Precision of 0.853, Recall of 0.877, and F1-Score of 0.865. The generative evaluation yielded a BLEU score of 0.9276 and ROUGE-L of 0.9301, indicating strong linguistic and semantic alignment between system outputs and expert-authored references. The study concludes that this approach offers a practical foundation for building AI-based legal assistance tools and highlights future opportunities for expansion to other legal domains and multilingual RAG applications.
Development of Color Segmentation and Texture Analysis Algorithms for Early Detection of Green Vegetable Deterioration in Retail Environments
Vegetable deterioration in retail environments is often accelerated by improper storage conditions, leading to quality degradation, economic losses, and reduced consumer trust. Early detection of deterioration is therefore essential to enable timely preventive actions before visible spoilage becomes severe. This study proposes an integrated image-based framework for early detection of spinach leaf deterioration by combining K-Means++ for robust color segmentation, Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction, and Convolutional Neural Network (CNN) for classification. K-Means++ improves segmentation stability through optimized centroid initialization, GLCM captures subtle texture variations associated with early spoilage, and CNN enables accurate classification by learning complex visual patterns from segmented images. The dataset consists of 642 spinach leaf images captured under controlled lighting for initial calibration and under varying lighting conditions to simulate real-world retail environments. Experimental results show that the standard K-Means algorithm achieved an average classification accuracy of 77%, while the proposed K-Means++ segmentation improved accuracy to 81.86%. Furthermore, CNN-based validation achieved the highest classification accuracy of 94.82%, demonstrating strong generalization capability. The novelty of this work lies in the optimized integration of K-Means++ segmentation under lighting variability, selective GLCM feature utilization validated through ablation analysis, and end-to-end CNN-based validation with real-time deployment feasibility. The proposed framework offers a practical, scalable, and non-destructive solution for automated freshness monitoring in retail environments and can be extended to other leafy vegetables.
Multidimensional Data-Driven Modeling of Sustainable E-Commerce Development with Direct and Interaction Effects
Sustainable e-commerce development has become a critical issue for emerging economies as digital markets expand rapidly, alongside growing concerns about regulatory effectiveness, trust, and resource efficiency. This study investigates the key factors influencing sustainable e-commerce development in Vietnam by explicitly integrating Institutional Theory, the Technology–Organization–Environment (TOE) framework, and the Resource-Based View (RBV) into a unified structural equation modeling (SEM) framework. Institutional Theory is operationalized through regulatory quality, capturing the role of formal rules and enforcement mechanisms in shaping market stability and legitimacy. The TOE framework is reflected in digital infrastructure, government support, and competitive pressure, which together represent technological readiness and environmental conditions. RBV is operationalized through resource availability and management capacity, emphasizing firms’ internal capabilities to sustain long-term e-commerce performance. In addition, trust is incorporated as both a direct determinant and a moderating mechanism that strengthens the effectiveness of institutional and organizational factors. A mixed-method research design was employed. The qualitative phase involved in-depth discussions with 35 policymakers, business managers, and e-commerce platform managers to refine the theoretical integration and measurement scales. Based on these insights, a structured questionnaire was administered to frequent online shoppers in Ho Chi Minh City and Dong Nai Province, yielding 653 valid responses. SEM results indicate that regulatory quality, digital infrastructure, government support, competitive pressure, resource availability, and trust all have significant positive effects on sustainable e-commerce development. Resource availability and regulatory quality exert the strongest impacts, while trust and management capacity significantly moderate the effects of regulatory quality and resource availability, respectively. By explicitly mapping institutional, technological, organizational, and relational constructs into a coherent SEM framework, this study provides a theoretically grounded and empirically validated model of sustainable e-commerce development in an emerging economy context. The findings offer valuable implications for policymakers and practitioners seeking to foster a resilient, trustworthy, and sustainable e-commerce ecosystem in Vietnam and similar developing economies.
RankPro-M Method to Alleviate the Sparsity Problem in Collaborative Filtering
The rapid shift from conventional commerce to online platforms has been driven by evolving consumer behavior that demands fast, accurate, and personalized services. Consequently, e-commerce has become a primary channel for product marketing and service delivery without temporal or spatial constraints. However, the continuous expansion of e-commerce platforms has led to a substantial increase in both the volume and diversity of available products, thereby complicating the task of delivering personalized recommendations aligned with user preferences. Recommender systems offer an effective solution to this challenge, with Collaborative Filtering (CF) being among the most widely adopted techniques. Despite its popularity, CF suffers from a critical limitation known as the data sparsity problem, which adversely affects recommendation accuracy and system reliability. This study proposes RankPro-M, a ranking-oriented imputation approach designed to mitigate the impact of sparsity in recommender systems. RankPro-M operates by identifying items with high rating frequency and imputing missing ratings using mode values as representations of dominant user preferences. The imputed rating matrix is subsequently processed through ranking aggregation mechanisms (Borda, Copeland, and WP-Rank) to generate item recommendations. Experimental results demonstrate that the application of RankPro-M consistently improves recommendation quality, as indicated by increased Normalized Discounted Cumulative Gain (NDCG) values across multiple evaluation scenarios. These findings confirm that RankPro-M effectively addresses data sparsity and enhances the performance of ranking-based recommender systems.
An Adaptive Random Forest for Data Stream Sentiment Classification under Concept Drift
Data labeling plays a crucial role in determining the performance of machine learning models, especially in data stream environments where concept drift frequently occurs. The primary objective of this study is to analyze the effectiveness of adaptive learning models in managing dynamic data distribution changes and to evaluate the influence of different labeling strategies on sentiment classification performance using user reviews from the OVO mobile application. The research contributes to understanding how labeling approaches interact with adaptive modeling under real-time data stream conditions. Two labeling methods were employed: score-based labeling derived from user ratings and content-based labeling generated automatically using the IndoRoBERTa language model. These labeled data streams were evaluated using two classifiers: a conventional Random Forest model and an Adaptive Random Forest model designed to handle evolving data distributions. The evaluation was conducted through streaming experiments that continuously fed new review data to simulate real-world drift scenarios. The results reveal that in the score-based labeling scenario, the conventional Random Forest model’s accuracy gradually declined, reaching a final accuracy of 31%, while the Adaptive Random Forest achieved 80%, reflecting a 49% performance gap. In the content-based labeling scenario, both models improved over time, with final accuracies of 57% for Random Forest and 76% for the adaptive model, resulting in a 19% difference. These findings indicate that Adaptive Random Forest is more robust in adapting to distributional and temporal changes in data streams regardless of the labeling strategy used. This study implies that combining adaptive learning with semantically rich labeling approaches can substantially enhance model reliability in real-time sentiment analysis tasks. Future research may further explore hybrid adaptive mechanisms to improve the resilience of data stream classification models across various domains.
SME Business Intelligence Support Using Retrieval-Augmented Generation and RFM Segmentation
This study presents the design and evaluation of a cloud-based business intelligence support system for small and medium enterprises that integrates retrieval-grounded text generation with recency–frequency–monetary customer segmentation to enhance digital customer communication and promotional decision making. The primary objective is to assist individual small businesses in responding accurately to customer inquiries while simultaneously leveraging historical transaction data to identify actionable customer groups, all within their existing messaging workflows through a mobile keyboard interface. The proposed framework combines two complementary components. The first component automatically generates customer replies by retrieving semantically relevant information from a structured business knowledge base and using it to produce grounded, context-aware responses. The second component analyzes invoice records to segment customers into loyal, moderate, and at-risk groups, enabling sellers to tailor promotional strategies based on observed purchasing behavior. The system is implemented as a cloud service accessed by individual enterprises without requiring local infrastructure or model training. System evaluation was conducted using real small business data collected over several weeks. Experimental procedures included retrieval faithfulness analysis, response correctness evaluation with confidence intervals, customer cluster validation using silhouette analysis, end-to-end latency measurement, and structured user acceptance testing. Performance results demonstrate that the retrieval mechanism consistently provides accurate knowledge grounding, while the segmentation module effectively distinguishes high-value and churn-risk customers. The average response time remained within a range perceived as acceptable for real-time mobile conversations, and user testing confirms that the keyboard-based interface does not disrupt normal communication practices. The findings indicate that embedding retrieval-grounded generation and lightweight customer analytics directly into daily messaging tools can significantly improve the operational efficiency of small enterprises. This integrated approach reduces the burden of manual response handling while enabling data-driven promotional decision making. The framework offers a practical pathway for adopting artificial intelligence in small business environments and provides a foundation for future enhancements such as temporal behavior modeling and multilingual support.
Applying Transfer Learning on Various GNN Model Training in Indoor Positioning System Tasks
Determining location and orientation has always been a fundamental challenge, driving advances from maps and compasses to modern global navigation satellite systems (GNSS). However, GNSS performs poorly indoors due to signal attenuation and lack of elevation accuracy, necessitating the development of indoor positioning systems (IPS). Various technologies such as Wi-Fi, Bluetooth Low Energy (BLE), and RFID have been deployed, typically relying on received signal strength (RSS) and fingerprinting to improve accuracy. While previous research focused on training a single model for an entire building, this study explores the creation of floor-specific models by applying transfer learning to various GNN models. This is done to address the substantial signal distortion between floors. Using the UTSIndoorLoc dataset, we evaluate Graph Attention Network (GAT), GraphSAGE, and Graph Convolutional Network (GraphConv) for predicting two-dimensional indoor positions based on RSSI fingerprints. We propose 2 transfer learning model training methods, Schema A and Schema B. Schema A trains the base model iteratively through each floor, and Schema B trains the base model on a unified dataset. Schema B with GraphConv achieved the best results with a mean positioning error of 6.2176 meters. Whilst Schema A achieved a best-case mean positioning error of 6.3900 meters. Both outperforming the standard unified model which has a mean positioning error of 8.0808 meters.
Improved Hybrid GoogLeNet-Based Deep Learning Optimization for Standardized Straw Mushroom Quality Classification in Indonesia
Deep learning plays a crucial role in modern computer vision due to its ability to automatically extract hierarchical features from large-scale image data. Among various architectures, Convolutional Neural Networks (CNNs) have been extensively utilized for image pattern interpretation, including in agricultural product inspection. Straw mushrooms (Volvariella volvacea) are important agro-industrial commodities in Indonesia; however, their quality assessment still relies on subjective manual evaluation based on the Indonesian National Standard (SNI:01-6945-2003), leading to inconsistency in grading results. To address this limitation, this research proposes an Improved Hybrid GoogLeNet model integrated with a YOLO-based detection framework and hybrid preprocessing to enhance feature clarity and classification robustness. The system is capable of conducting object detection, 3-class morphological quality classification (Pure White, Oval, and Black Spot/Defect), and automatic diameter measurement using calibrated pixel-to-centimeter conversion. Performance evaluation is carried out by benchmarking the proposed model against several popular deep learning architectures including YOLOv5, LeNet, AlexNet, VGGNet, and ResNet. Experimental results demonstrate that the Improved Hybrid GoogLeNet achieves the highest performance with precision of 97.99%, recall of 96.07%, and F1-score of 96.98%, along with low misclassification rates across all classes. These results indicate that the proposed method provides accurate, reliable, and efficient quality assessment that supports standardized automated grading in industrial applications. Therefore, this study contributes to the advancement of intelligent computer vision solutions for digital transformation in the Indonesian mushroom agro-industry.
Utilization of K-means Clustering for Classifying Diabetes Risk Populations According to Health Behaviors and 3Es-2Ss Health Literacy
This study focused on classifying populations at risk for diabetes using K-means clustering integrated with the 3Es–2Ss health literacy framework: eating, exercise, emotion, smoking cessation, and alcohol cessation. Biological, behavioral, and health literacy data were analyzed. The dataset was collected from 126 participants identified as at-risk individuals in Ngao District, Lampang Province, Thailand. This relatively small, community-based sample provides valuable insights into local health behaviors but limits the generalizability and statistical power of the findings to broader populations. The K-means clustering analysis, guided by the Elbow method, identified k = 4 as the optimal number of clusters, yielding four distinct groups with different socio-demographic and health characteristics. These clusters revealed variations in health profiles, economic status, and behavioral literacy within the Thai population. Despite the small sample size and limited generalizability, missing data and inconsistencies were systematically addressed through data cleaning and normalization to maintain analytical reliability. The results suggest that K-means clustering can serve as an effective decision-support tool for public health planning, particularly for Non-Communicable Disease (NCD) prevention and diabetes management at the local level.
A Hybrid Method for Low-Resource Named Entity Recognition
Named Entity Recognition (NER) is a critical component of Natural Language Processing with diverse applications in information extraction and conversational AI. However, NER in specific domains for low-resource languages faces challenges such as limited annotated data and heterogeneous label sets. This study addresses these issues by proposing a hybrid neurosymbolic framework that integrates rule-based processing with deep learning models for Vietnamese NER. The core idea involves a two-stage pipeline: first, a rule-based component reduces label complexity by grouping relational and special categories; second, pre-trained language models are fine-tuned for high-precision extraction. A post-processing module is then utilized to restore fine-grained labels, preserving expressiveness for application-level usability. To mitigate data scarcity, a scalable data augmentation strategy leveraging Large Language Models (LLMs) is introduced to expand the label set without full re-annotation—a significant novelty of this work. The effectiveness of this method was evaluated across five specific-domain datasets, including logistics, wildlife, and healthcare. Experimental results demonstrate substantial improvements over strong RoBERTa-based baselines. Specifically, the proposed system achieved F1 scores of 90% in Customer Service (up from 83%), 84% in GAM (up from 73%), 83% in AI Fluent (up from 80%), 94% in PhoNER_Covid19 (up from 91%), and 60% in Rare Wildlife (up from 36%). These findings confirm that the hybrid approach effectively captures the linguistic complexity of Vietnamese and contextual nuances in specialized domains, offering a robust contribution to low-resource NER research.
Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method
This study aims to develop an intelligent system capable of recognizing traditional woven motifs of Puta Dino, a culturally significant textile from Tidore Island. These motifs are visually complex, poorly documented, and hard for the public to distinguish, highlighting the need for a digital tool to support cultural preservation and accurate identification. This research is the first to build a structured Puta Dino motif database and provide an integrated model designed for real-world use. The approach captured primary images of eight validated motifs and applied systematic preprocessing, including normalization and data augmentation, to enhance variability and strengthen the dataset. A lightweight deep learning model predicated on a convolutional neural network was designed to achieve a compromise between accuracy and computational efficiency. The system was evaluated through cross-validation and independent test data, as well as multiple real-world trials utilizing a web interface. These trials involved different image capture scenarios, including from a distance, moderate distance, close and angled views, and when the fabric surface was folded. The model architecture and system interface with the system are illustrated in the relevant figures, and the tables provide performance data on the system’s training, accuracy in motif classification, and achieved results in real-world conditions. The system demonstrated excellent classification accuracy in controlled test conditions. It showed real-world competency, accurately classifying most motifs in various conditions. The data also point to specific issues with motif recognition in extreme distortion cases, which reflect the typical issues of laboratory-to-field model deployment. The outcomes clearly demonstrate both the possibilities and the limitations of the currently available recognition of culturally significant textiles. The study concludes by exploring the possibilities of expanding the dataset and increasing the depth of learning through more sophisticated techniques, as well as enhancing accessibility to promote sustained community and cultural engagement.
Determinants of Student Behavior to Use Financial Technology (Fintech) Banking Services - Integrated Theory of System Acceptance and Psychological Behavioral Theory
Fintech provides technology-based banking and financial services; therefore, the analysis of FinTech usage behavior should be viewed in the context of system acceptance and psychological behavioral theory. We employ the System Acceptance Theory approach, specifically the Theory of Acceptance Model, and the psychological behavioral theory, the Theory of Reasoned Action, to explain behavioral intention to use FinTech and incorporate risk factors. This study aims to prove the influence of perceived ease of use, perceived usefulness, subjective norms, attitude, and perceived risk on the intensity of Generation Z's intention to use Fintech. Moreover, this research demonstrates the influence of intention to use Fintech on fintech usage behavior. This research employed a survey approach with 350 students in Indonesia, who are part of Generation Z, and analyzed the data using Structural Equation Modeling with Partial Least Squares. We report that perceived ease of use and perceived usefulness are vital factors in increasing the intention to use Fintech. Attitude is a factor that encourages students to use Fintech, and conversely, perceived risk is a vital factor in decreasing intention to use Fintech. We were unable to find evidence of a relationship between subjective norms and intention to use Fintech. Ultimately, behavioral intention in using Fintech is crucial for increasing student adoption of Fintech. This study recommends that financial institutions offer Fintech services to enhance usability, convenience, and mitigate the risks associated with fintech use.
An Integrated Text Analytics and Ensemble Machine Learning Framework for Fake Review Detection in Online Marketplaces
The increasing prevalence of fake reviews on e-commerce platforms undermines consumer trust and affects purchasing decisions, particularly for local products by limited visibility such as those by West Sumatra, Indonesia. This study proposes a hybrid approach combining text analytics and machine learning to enhance the detection of fake reviews. Four classification models—Naive Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbor—were tested on a dataset of 1,500 labeled product reviews. Among these models, Random Forest had the highest starting accuracy of 0.8533. To enhance it, we created a better algorithm called EKAHypeRFor (Enhanced Knowledge Augmentation of Hyperparameter Random Forest). This method uses simple feature engineering and careful tuning of settings by RandomizedSearchCV. The enhanced model reached an accuracy of 0.8778, which is 2.45% higher than the original. It also includes a real-time review sorting tool, making it easy to use on online shopping sites. Tests by a confusion matrix and feature importance drawn the model works well and is easy to understand. This method is simple, fast, and accurate, helping to make online product reviews more trustworthy for small and medium businesses in the area.
Comparison of Multilingual Model Sensitivity for Political Fact Verification with Integrated Multi-Evidence
Political news is frequently targeted by the dissemination of fake news on social media, which can influence public opinion and undermine trust in democratic processes. The main challenge in addressing this issue lies in the limited sensitivity of cross-lingual fact verification models in capturing semantic relationships between claims and evidence in long-text, multi-evidence settings. Existing approaches often struggle to assess the relevance and quality of evidence, resulting in suboptimal verification performance. This study compares three multilingual Large Language Models (LLMs), namely mBERT, XLM-R, and LaBSE, for political fact verification using an integrated multi-evidence approach. Experiments are conducted on the PolitiFact dataset, with performance evaluated using sensitivity, accuracy, precision, and F1-score metrics.The results indicate that mBERT achieves the highest overall sensitivity at 89.44%, followed by LaBSE at 81.81% and XLM-R at 78.81%. However, mBERT exhibits lower precision, whereas LaBSE provides a better balance between precision (87.02%) and accuracy (86.46%), resulting in an F1-score of 84.33%. XLM-R demonstrates lower sensitivity but maintains competitive precision (85.47%) and accuracy (84.60%), with an F1-score of 82.00%. Sensitivity analysis based on the number of evidence reveals distinct model behaviors, where mBERT performs optimally with six pieces of evidence, XLM-R is more effective under limited evidence conditions, and LaBSE shows a stable and increasing sensitivity trend as the amount of evidence increases, indicating robustness in multi-evidence scenarios. Further statistical analysis shows that XLM-R has the lowest performance variance, while LaBSE statistically outperforms mBERT in several evaluation aspects. Overall, LaBSE is recommended as the most balanced model for multi-evidence-based political fact verification.
A Hybrid YOLO–CNN Model for Automatic Detection and Severity Assessment of Atopic Dermatitis in Infant Images
Atopic dermatitis is one of the most common skin diseases affecting infants and children worldwide and has a particularly high prevalence in tropical countries. Traditional diagnosis methods, which still rely on physical examinations and laboratory tests often face challenges such as delays, high costs, and limited facilities, thereby necessitating an artificial intelligence–based system that is more efficient and accurate. This study aims to develop a hybrid YOLO–CNN model for the automatic detection and severity classification of atopic dermatitis in infants. The dataset comprises 2,000 infant skin images, including lesions categorized as mild, moderate, and severe, obtained from an online repository and field observations conducted in three villages. The labeling process was performed by a specialist doctor to ensure clinical validity. In the first step, YOLO was used to detect the lesion area in real time by generating a bounding box. This produced a region of interest (ROI), which was subsequently analyzed by a CNN model employing transfer learning in the second step to determine the severity level. Experimental results indicate that YOLO achieved high detection performance, with an mAP@0.5 of 91.2% and an F1-score of 90.2%, while the CNN model attained an average accuracy of 85% and a macro-F1 score of 85% in classification. The visualization of predictions indicates that most lesions were detected with confidence levels ≥0.9, confirming the model’s consistency. These findings highlight the potential of the hybrid YOLO–CNN framework as a supportive system for digital clinical diagnosis, applicable to both mobile applications and teledermatology services, particularly in regions with limited medical personnel. Future research should employ larger, multi-center datasets and integrate explainable AI approaches to promote broader clinical adoption.
Comparing Pre-Norm and Post-Norm Transformers in Preserving Gender Information for Indonesian–English Translation through Attention-Based Signal Reinforcement
Gender realization in Indonesian–English machine translation remains challenging due to the absence of grammatical gender in Indonesian, which often leads to unstable or ambiguous gender representations in English outputs. While Transformer-based models have demonstrated strong general translation performance, their ability to preserve gender information across encoding layers remains inconsistent and poorly understood, particularly with respect to architectural normalization strategies. This study presents a comparative analysis of Pre-Norm and Post-Norm Transformer architectures in preserving gender information, and examines the role of attention-based signal reinforcement in mitigating representational degradation. The reinforcement mechanism is introduced prior to standard encoder processing to strengthen gender-relevant token interactions without modifying the overall model structure. Four controlled configurations—Post-Norm, Pre-Norm, Post-Norm with attention-based reinforcement, and Pre-Norm with attention-based reinforcement—are trained under identical random seeds on both unbalanced and balanced datasets. Evaluation is performed on gender-ambiguous test sentences without explicit gender annotations to assess generalization. Gender preservation is assessed at the output level using gender-specific accuracy and BLEU score, and at the representation level using cosine similarity between gender cue embeddings and English gendered pronouns. The results show that Post-Norm Transformers fail to maintain stable gender representations, yielding near-random gender accuracy (~50%) and negligible BLEU scores. Pre-Norm architectures improve training stability but achieve limited gender accuracy (around 30%). Incorporating attention-based signal reinforcement substantially enhances gender preservation, with accuracy rising to over 50% and reaching up to 56% under balanced training conditions, accompanied by a consistent increase in cosine similarity values (exceeding 0.35) between gender cues and corresponding pronouns. These findings indicate that normalization strategy and attention-based reinforcement jointly determine the stability of gender representations in Transformer-based machine translation.
Adaptive Integration of Optuna Optimization and Stacking Ensemble Learning for Automated Work Competency Classification
Artificial intelligence and machine learning are increasingly used to automate analytical and decision processes, including the evaluation of human competencies. However, traditional models often face challenges in accuracy and generalization when applied to linguistic data from interviews. This study aims to develop a model that integrates Optuna optimization and stacking ensemble learning to enhance the accuracy and interpretability of competency classification. Interview transcript data were processed using natural language processing techniques such as cleaning, tokenization, case folding, stopword removal, and stemming to ensure textual consistency. The text was then transformed into numerical representations using term frequency inverse document frequency weighting. To handle class imbalance, the synthetic minority oversampling technique was employed. Optuna was applied to optimize the hyperparameters of base models, including support vector classifier, Naïve Bayes, random forest, gradient boosting, and XGBoost. These optimized models were combined through a stacking ensemble to form the final classifier. The proposed model achieved an accuracy of 94 percent and a precision of 95 percent with macro and weighted F1 scores of 0.94. The results demonstrate stable and balanced performance across all competency categories, including analytical thinking, initiating action, problem solving, and work standards. Comparative analysis with previous studies in sentiment analysis, medical diagnosis, and financial forecasting confirmed that the integration of Optuna and stacking produces more robust and generalizable outcomes. The integration of Optuna optimization and stacking ensemble learning effectively improves classification performance while maintaining interpretability. The model demonstrates strong potential for automated competency evaluation in recruitment and human resource analytics. This framework can be extended to other linguistic datasets to support transparent and data-driven decision-making in artificial intelligence applications.
Forecasting Bank Efficiency Using Data Envelopment Analysis with Directional Distance Functions and Machine Learning: Time-Series Validation and Shapley Value Interpretation
This study develops a structured framework to forecast the operational efficiency of commercial banks in Vietnam. The analysis is based on a balanced panel of 27 banks over the period 2016–2024. Bank efficiency is first measured using a directional distance function within a data envelopment analysis framework (DEA – DDF). This approach incorporates both desirable outputs and undesirable outputs related to credit risk. The estimated efficiency scores are then used as prediction targets in several machine learning models. Model performance is evaluated under both conventional test settings and time-series cross-validation, and predictions are interpreted using Shapley value–based analysis (SHAP). Under a conventional test set, the gradient boosting model (XGBoost) shows the best performance, with a root mean squared error of 0.060 and a coefficient of determination (R²) of 0.353. However, when time-series cross-validation is applied to reflect realistic forecasting conditions, predictive accuracy declines sharply. The average coefficient of determination falls to approximately 0.005. This suggests that static validation can overstate performance and that forecasting efficiency in a changing financial environment remains difficult. The interpretation results provide additional insights. Net interest margin has a positive effect on predicted efficiency, although the effect weakens at very high levels. The cost-to-income ratio shows a threshold around 0.55, beyond which efficiency declines more strongly. Bank size has a largely neutral impact. The interaction between capital adequacy and profitability shows a conditionally negative pattern. Prediction errors are larger in the most recent year and among banks with very high efficiency scores. In summary, the results highlight both the potential and the limitations of machine learning in forecasting efficiency and emphasize the importance of time-aware validation.
Automatic Analysis of Political Discourse: A Comparative Study of Multilingual and Large Language Models
This paper proposes the growing importance of automated analysis of political discourse in low-resource languages, using the Kazakh language as a case study. As political communication in Kazakhstan has increasingly moved online between 2019 and 2023, the need for accurate tools to evaluate political sentiment has grown. However, limited linguistic resources in Kazakh have hindered tool development. This paper introduces the first annotated corpus of political discourse in Kazakh, comprising 3,022 sentences selected from official statements, televised debates, policy documents, and social media publications. Each text was manually annotated for political sentiment by expert linguists and political scientists, with inter-annotator agreement measured to confirm reliability. Two main methodological approaches were employed for automatic sentiment classification: adapting multilingual neural network models to the Kazakh corpus and testing advanced generative language models in scenarios with minimal training examples. Performance was evaluated using standard classification procedures. The inclusion of pragmatic features such as code-switching, rhetorical emphasis, and discursive context led to notable improvements in classification accuracy. Experimental results demonstrate that models adapted to multilingual input achieved high classification quality, with fine-tuned multilingual transformer models reaching F₁-scores of up to 0.90, while large language models reached an F₁-score of 0.94 in few-shot settings. Explicit modeling of code-switching and pragmatic features yielded an improvement of approximately 4 percentage points in F₁. This research contributes a practical resource and a methodological framework for analyzing political sentiment in underrepresented languages, highlighting the feasibility of developing high-quality automated tools for political text analysis without extensive training data.
Psychometric Validation of an AI-Based Evaluation System for Identifying Discrepancies in Learning Processes
This research advances the field of educational evaluation by designing and psychometrically validating an artificial intelligence (AI)- based diagnostic tool to detect discrepancies in university learning processes. The main novelty is the integration of the Provus Discrepancy Model combined with a forward-chaining inference engine. This research aims to transform evaluation from an administrative activity to an ongoing process of improvement. The tool was developed and validated through a sequential mixed-methods approach with 400 participants from 3 state universities and 8 evaluation experts. Results from the study provide evidence that the validated system created a substantial range of psychometric characteristics. These psychometric characteristics include strong content validity (SD-CVI/Ave = 0.94); high internal consistency and reliability (Cronbach's α = 0.94); solid construct validity as demonstrated through Confirmatory Factor Analysis (CFA) (CFI = 0.94; RMSEA = 0.054) and a substantial range of predictive analytics (diagnostic learning analytics), which the AI learning analytics engine evaluated learning discrepancies with a 92.4% diagnostic accuracy (47.4% more accurate than manual evaluation methods). The system's validated usefulness is demonstrated through high system usability (SUS = 88.2); high practical utility (85% total score on the Pragmatic Utility Assessment); significant utility (real-world) practical utility (detected 45 discrepancy patterns), cost efficiency (73% cost and 67% analysis time compared to traditional methods), and a range of analytics (predictive and learning discrepancy analytics). The significant contribution of this study is the development of the world's first integrated AI evaluation system that meets high methodological and psychometric standards, along with a set of real-time diagnostic analytics. Ultimately, this study developed the first truly integrated, novel paradigm evaluation system that combined the historically established evaluation construct and mechanisms with the most advanced AI capabilities, providing educators and institutions with evaluation tools to deliver data-driven pedagogical strategies and interventions in higher education.
A Study on the Mechanism of Virtual Anchors' Interactivity and Attractiveness Influencing Consumer Trust, Emotional Attitudes, and Purchase Intentions
This study presents a dual-mediation model examining how the interactivity and appeal of virtual streamers affect consumers' purchase intentions. The model is anchored in the "Stimulus-Organism-Response" (S-O-R) framework, seeking to clarify and compare the efficacy of two distinct psychological pathways: cognitive, mediated by perceived trust, and affective, mediated by emotional attitude. A survey of 515 Chinese consumers with prior exposure to virtual streamer e-commerce livestreams was conducted, with the model tested using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings demonstrate that both interactivity and appeal significantly increase perceived trust and cultivate positive emotional attitudes. Notably, the direct effect of perceived trust on purchase intention is more potent than that of emotional attitude. Intermediary analysis further confirms that both paths are important intermediaries, and the trust intermediary path consistently exerts stronger influence. These results show that in the context of AI-driven virtual anchors, the cognitive-based trust path is more influential than the emotional-based path. By comparing these two paths, this study has improved our theoretical understanding of virtual persuasion and provided a new contribution. In fact, this study puts forward a strategy of "trust first, emotional strengthening" for marketers who use virtual anchors. Future research should investigate the cross-cultural applicability of these findings and the moderating effect of product categories.
Artificial Intelligence Applications and Digital Finance Development: The Moderating Role of Human Resources and Digital Infrastructure
The rapid advancement of digital technologies has fundamentally transformed financial systems, particularly in emerging economies, where digital finance plays a critical role in enhancing financial accessibility and efficiency. Among these technologies, artificial intelligence (AI) has emerged as a strategic driver reshaping digital financial services. This study aims to investigate the direct and moderating effects of artificial intelligence applications on the development of digital finance by integrating technological, human, institutional, and innovation perspectives. A sequential mixed-methods design was employed. In the qualitative phase, semi-structured interviews were conducted with 55 experts in banking, finance, and financial technology. The sample size was determined based on theoretical saturation, which was reached when successive interviews yielded no substantially new insights regarding construct dimensions or measurement refinement. Insights from this phase were used to validate constructs and refine the measurement instrument. In the quantitative phase, survey data were collected from 700 digital banking users across 20 commercial banks in Vietnam. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test the proposed hypotheses. The results indicate that the digital policy framework (β = 0.300) and the digital human resource management (β = 0.279) exhibit the largest direct effects on digital finance development, based on the relative magnitude of standardized path coefficients. Among these factors, digital policy frameworks and digital human resource management demonstrate the strongest direct impacts. More importantly, the findings confirm the moderating role of artificial intelligence applications. AI significantly strengthens the relationships between digital human resource management and digital finance development, as well as between digital technology infrastructure and digital finance development. These results indicate that AI serves not only as an independent technological driver but also as a strategic catalyst, enhancing the effectiveness of digital infrastructure and human capital. This study contributes to the digital finance and information systems literature by empirically demonstrating that artificial intelligence serves as both a determinant and a moderator of digital finance development. From a practical perspective, the findings suggest that policymakers and banking executives should prioritize AI-enabled human resource strategies and the intelligent use of digital infrastructure to accelerate the development of sustainable digital finance in emerging economies.
Adaptive k-Nearest Neighbor Learning for Robust Modal Regression on Multimodal and Heavy-Tailed Data
Modal regression has attracted increasing attention as an alternative to mean-based regression, particularly in settings characterized by heteroscedasticity, multimodal conditional distributions, and heavy-tailed noise. In such scenarios, estimators based on central tendency may yield predictions that fall in low-density regions of the response space. This paper proposes an adaptive k-nearest neighbor framework for modal regression that integrates entropy-guided neighborhood selection with nonparametric mode estimation, including MeanShift clustering and one-dimensional kernel density estimation. The proposed approach adjusts neighborhood size based on local uncertainty, allowing the regression model to adapt to variations in data density without relying on a globally fixed parameter. Extensive experiments on simulated datasets and real-world benchmarks demonstrate that adaptive modal regression methods generally reduce or stabilize prediction errors relative to fixed-k modal regression and classical kNN mean and median estimators, particularly under heteroscedastic and multimodal conditions, although the magnitude of improvement varies across scenarios. Statistical tests confirm significant differences in most experimental settings, with practical gains ranging from incremental to substantial depending on data complexity. In addition to accuracy, computational behavior is explicitly examined. The findings show a trade-off between computational cost and predictive robustness: entropy-guided adaptive modal regression requires additional runtime due to neighborhood adaptation and density estimation, but this overhead increases proportionally with sample size and remains manageable for medium-sized datasets. Based on these results, adaptive modal regression provides a useful and flexible alternative for regression tasks involving complex and heterogeneous data distributions where robustness is prioritized over minimal computation time.
Iris Identification Using Resnet Iris Feature Extraction Architecture For Better Biometric Security
Iris recognition is widely acknowledged as one of the most reliable biometric modalities due to its high uniqueness, rich textural patterns, and long-term stability. Unlike other biometric traits, iris characteristics resist forgery, aging effects, and environmental variations, making it suitable for high-security applications. Recently, convolutional neural networks (CNNs) have been extensively applied in iris recognition to improve feature representation and classification accuracy. However, many CNN-based approaches still depend on conventional segmentation and handcrafted features, which reduce robustness under noisy data, illumination variations, occlusions, or unconstrained environments. To address these limitations, this study proposes an enhanced iris identification framework combining a modified T-Net for precise segmentation with deep residual feature extraction for improved discrimination. Unlike conventional systems focus mainly on classification, the proposed approach emphasizes segmentation-driven feature consistency, ensuring extracted features originate from accurately localized iris regions. This design enhances stability and reliability, particularly under challenging imaging conditions. The framework leverages transfer learning and efficient representation learning strategies, enabling high accuracy even with a limited labelled data. Evaluations on three benchmark datasets CASIA-IrisV4, IITD Iris Database, and UBIRIS.v2 covering both controlled and less-constrained acquisition scenarios. Results show that it achieves classification accuracy of up to 98.35%, while maintaining computational efficiency suitable for deployment. The proposed architecture offers a robust, data-efficient, and scalable solution for secure biometric authentication, with strong potential for real-world applications such as access control, identity verification, and high-security authentication systems.
A Hybrid Fuzzy-LLM Framework for Difficulty Estimation of Math Word Problems: A Data-Driven Human-in-the-Loop Study
Assessing the difficulty levels of Math Word Problems (MWPs) is essential for adaptive learning, yet most existing MWP datasets lack standardized difficulty annotations. This paper proposes a decision framework that integrates a 2-tuple Fuzzy Linguistic Decision Model (FLDM) with Large Language Models (LLMs) for automated difficulty estimation. A corpus of over 2,000 MWPs was compiled, of which 200 were annotated by seven instructors and an additional 454 were validated by ten experts. Consensus stability improved markedly (Fleiss’ κ = 0.14 → Cohen’s κ = 0.32), reflecting stronger alignment between expert judgments and the proposed fuzzy 2-tuple aggregation. Sixteen LLM configurations were evaluated, including GPT-3.5, GPT-4o-Mini, Gemini Flash, and LLaMA-3.2 under Zero-Shot, Five-Shot, and RAG settings. GPT-3.5 Zero-Shot achieved the best performance (Precision=0.65, Recall=0.63, F1=0.63), outperforming GPT-4o-Mini and Gemini variants. The validated dataset and linguistic ground truth were integrated into a web-based annotation system (themathbits.com), demonstrating scalability for real-world deployment. The results show that combining human linguistic judgments with fuzzy modeling and LLM inference improves reliability of MWP difficulty estimation, providing a foundation for future adaptive learning platforms.
A Hybrid Multi-Criteria and Factorial Analysis Framework for CIRFLINK CubeSats Communication Design
This research presents the design and optimization of a low-cost Inter-Satellite Link for the CIRFLINK project, a 1.5U CubeSat mission aimed at advancing smart farming and disaster monitoring in rural area. To address complex design trade-offs, this study introduces a novel integrated framework combining the Analytic Hierarchy Process for qualitative prioritization and 2k factorial design for quantitative validation. The primary objective was to evaluate and optimize LoRa-based communication parameters for constrained satellite environments. Analytic Hierarchy Process results prioritized LoRa technology over optical and traditional RF subtypes due to its superior power efficiency and simplicity. Subsequently, 2k factorial experiments and ANOVA revealed that distance and physical obstruction are the dominant factors affecting performance, while parameters like Spreading Factor and Bandwidth showed less immediate impact in the tested ranges. Experimental results using ESP32 and SX1278 modules demonstrated that the Signal-to-Noise Ratio and Received Signal Strength Indicator maintain reliable connectivity, with correlation analysis showing a strong negative relationship approximate -0.913b etween distance and signal quality. Field data confirmed that the system achieves stable communication with an average SNR in line-of-sight conditions. The novelty of this work lies in the systematic fusion of multi-criteria decision-making with statistical experimental design, providing a transparent engineering roadmap for small-satellite communication. This contribution offers a validated, cost-effective ISL solution that meets mission requirements with minimal complexity, serving as a scalable model for future educational and IoT-based CubeSat constellations.
Predicting 2000-Meter Indoor Rowing Performance Using Accessible Machine Learning Models
The 2000-meter ergometer test is widely used to measure athlete's strength, skill and efficiency in competitive rowing. Traditional tests like 500m or 1000m rowing can be too physical and elaborate for beginners or younger rowers. This study aimed to create a simpler and data driven approach to predict 2000m rowing times using basic information like age, gender and weights. Predictions were made using machine learning models including XGBoost that were applied to data from 1,341 rowers obtained from Concept 2 Database and Miami Beach Rowing Club. The model performed better for athletes over 18 years old with gender as the most important factor followed by weight and age. Finally after rigorous model training, the model showed insightful prediction accuracy with R2=0.75, MAE=0.35 min and RMSE=0.47 min. However, cross-validation of the model showed R2=-2.04, indicating overfitting due to limited variables and data. Despite this limitation, our model offers a practical application that can help rowers set realistic goals and assist coaches in personalized training. In conclusion, the model can still be improved to improve accuracy and validation but in the current study it represents a step forward in making performance insights more accessible to rowers.
Artificial Intelligence, Transformational Leadership, and Job Performance: Mediating Role of Job Engagement and Moderating Role of Work Passion
This study investigates the relationships among Artificial Intelligence (AI), perceived usefulness of AI, transformational leadership, job engagement, and job performance, with the moderating role of work passion. Drawing on the Job Demands–Resources (JD–R) model and the Technology Acceptance Model (TAM), the study proposes a research model explaining how technological and leadership resources jointly influence employee performance in the context of digital transformation. A quantitative approach was employed, with data collected through an online survey of 345 employees at five leading joint-stock commercial banks in Vietnam. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test the proposed hypotheses. The findings reveal that perceived usefulness of AI is the strongest indirect predictor of job performance through the mediating role of job engagement. The results also confirm that transformational leadership significantly enhances employee engagement, particularly through inspirational motivation and individualized consideration. Artificial Intelligence, as an organizational resource, further strengthens engagement by reducing workload and supporting decision-making processes. Furthermore, work passion plays a moderating role in the relationship between job engagement and job performance, with harmonious passion amplifying this relationship while obsessive passion may reduce its marginal effect. These findings highlight the importance of integrating AI applications with effective leadership practices to foster employee engagement and improve job performance in modern digital organizations.
Interpretable Temporal Risk Modeling for Contributor Inactivity Prediction: A Comparative Study of Tree-Based Ensembles
This study aims to develop an interpretable temporal risk modeling framework for predicting contributor inactivity in collaborative development environments, thereby supporting sustained participation and improving productivity. The research focuses on contributor activity data collected from a collaborative software development platform, in which participation histories are represented by temporal engagement features that capture activity recency, participation intensity, and contribution patterns over time. To model inactivity risk, several tree-based ensemble learning algorithms, including Random Forest, XGBoost, LightGBM, and a stacking ensemble, are employed and evaluated under imbalanced classification conditions. Experimental results demonstrate strong predictive performance across models, with Random Forest achieving the highest AUC of 0.9401, while XGBoost obtains the best Matthews Correlation Coefficient (0.7353). The novelty of this study lies in prioritizing structured temporal behavioral representation through normalized temporal engagement features rather than increasing model complexity, enabling more interpretable inactivity risk modeling. The findings provide practical implications for collaborative platform managers by enabling early identification of contributor disengagement, supporting sustained participation, improving productivity, and facilitating continuous product innovation.
From Luxury to Mass Market: How Brand Love and Luxury Perception Drive Purchase Intentions Through TPB
This study examines psychological mechanisms shaping consumer purchase intentions for mass market products from former luxury brands. Grounded in the Theory of Planned Behavior and attachment theory, we investigate how luxury brand perception and brand love influence attitudes and subjective norms as antecedents of behavioral intention. Data were collected from 868 Jakarta consumers through an online survey and analyzed using partial least squares structural equation modeling to assess measurement and structural relationships. Results indicate that luxury brand perception exerts significant direct effects on purchase intention and indirect effects mediated specifically through attitude and subjective norm. Brand love influences purchase intention exclusively through these same attitudinal and normative pathways, with no significant direct effect observed. Both attitude and subjective norm significantly mediate the relationships between luxury perception, brand love, and purchase intention. Contrary to expectations, self-referencing does not moderate the attitude intention or subjective norm intention relationships, suggesting limited influence of self-related cognitive processing in this context. Theoretically, this research advances the Theory of Planned Behavior by positioning brand love as an antecedent rather than an outcome of attitudes and subjective norms, thereby integrating emotional attachment as a foundational driver within rational decision frameworks. Managerially, findings suggest that luxury brands entering mass markets should prioritize preserving symbolic heritage and cultivating emotional bonds while leveraging social validation mechanisms to translate brand love into actual purchase behavior. Limitations include the cross-sectional design restricting causal inference and the single culture Jakarta sample limiting generalizability. Future research should employ longitudinal and cross-cultural designs to examine dynamic emotional attachment processes and test model robustness across diverse consumer contexts.
Adaptive Marker-Controlled Watershed Combined with Voxel Quantification for Automated Fetal Measurement
Accurate and consistent fetal biometric measurement is essential for assessing fetal growth and gestational age in prenatal care. However, ultrasound (US) imaging presents several challenges, including speckle noise, shadowing artifacts, and low tissue contrast, which often degrade segmentation accuracy. Classical watershed algorithms, though effective for edge detection, tend to produce over-segmentation in such complex textures. The dataset used in this study consisted of 272 ultrasound images of patients from M. Djamil Hospital, Padang, West Sumatra. The dataset covers various phases of fetal development, from the first trimester to the third trimester. All images correspond exclusively to fetal ultrasound examinations and were used solely for automated fetal biometric analysis. To overcome these issues, this study introduced an Adaptive Marker-Controlled Watershed (AMCW) algorithm combined with Voxel Quantification (VQ) to achieve more reliable and automated fetal measurements. The proposed AMCW method integrates adaptive marker generation based on morphological gradient and local intensity statistics, enabling dynamic control of internal and external markers across varying fetal regions. After segmentation, spatially calibrated pixel-based quantification was employed to estimate the dimensional properties of segmented fetal structures. The method was applied exclusively to 2D B-mode ultrasound datasets across multiple gestational ages, targeting four key fetal parameters: Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL). Although the present study is limited to 2D ultrasound images, the proposed framework may be extendable to 3D ultrasound data in future research. The combination of adaptive marker-controlled watershed segmentation and voxel-based quantification presents a robust, interpretable, and computationally efficient framework for automated fetal measurement. The CNN achieved a classification accuracy of 98.75% on the independent testing dataset, indicating that the extracted biometric features contain strong discriminative information for automated fetal condition assessment. This hybrid approach minimizes operator dependency and measurement variability aligning with clinical measurement trends.
Cognitive and Technological Factors Shaping Students’ Sustained Use of ChatGPT in Higher Education
This study examines the cognitive and technological factors shaping students' sustained use of ChatGPT in Indonesian higher education. Despite the rapid adoption of generative Artificial Intelligence (AI) in education, a clear understanding of the factors sustaining continued engagement with such systems remains limited. While continuance intention has been widely examined, the application of the Expectation–Confirmation Model (ECM) in generative AI contexts remains underexplored. This gap is especially evident when considering the role of AI-specific system attributes in shaping post-adoption evaluations. Although ECM has been extended with various constructs in prior studies, the specific integration of AI characteristics, particularly perceived intelligence and anthropomorphism, has not been explored in generative AI use in education, especially within Indonesian higher education. To address this gap, a multi-theoretic framework integrating ECM and AI characteristics was developed. Data from 322 Indonesian students were analyzed using Partial Least Squares-Structural Equation Modeling. All ten hypotheses were supported, and the model explains 43.3% of the variance in continuance intention (R² = 0.433). Perceived Intelligence strongly influences Perceived Anthropomorphism with a path coefficient of 0.591, representing the strongest relationship in the model, while other paths demonstrate moderate or modest effects. The findings confirm ECM's robustness in generative AI settings and highlight the pivotal role of AI characteristics in shaping post-adoption evaluations and sustained use. These results contribute to the growing body of research on generative AI adoption in education by demonstrating how system intelligence and human-like interaction jointly influence continuance intention. The findings also offer practical guidance for AI developers to enhance system intelligence and natural interaction. Future research could explore how students experience AI over time and what shapes their sustained use using different research methods.
Monkeypox Disease Classification Based on Skin Images Using Hierarchical Swin Transformer-Based Convolutional Neural Network Approach
Monkeypox diagnosis can initially be conducted through expert physical examination based on characteristic lesions. However, laboratory confirmation using PCR is still essential, these tests are often hampered by limitations such as high costs, lengthy processing times, and a general lack of detailed symptom knowledge among patients. In light of these issues, image-based diagnostic methods offer a more efficient solution, given that monkeypox manifests as visible lesions on the skin that can be accurately detected using a deep learning. This study employs Transformer network-based deep learning for classifying skin diseases. To improve model robustness and mitigate the limitations of the relatively small dataset, we designed a comprehensive data augmentation pipeline that incorporates both positional and color transformations, including rotation, horizontal and vertical flipping, zooming, shearing, and brightness, contrast, hue, and saturation adjustments. Furthermore, a k-fold cross-validation strategy was employed, where the entire dataset was partitioned into k equal-sized folds to ensure a reliable and unbiased evaluation of the model performance. The Swin Transformer leverages advanced transformer network to analyze images, emphasizing hierarchical relationships within images. Swin Transformer enhances the convolutional Transformer architecture by substituting the standard multi-head-self-attention (MSA) mechanism with a shifted window-based MSA module It enhances efficiency over traditional transformer models by incorporating a shifted window mechanism, which reduces computational demands. The average global accuracy achieved was 0.99 (99%), which is further supported by the AUC values obtained for each disease category. The model achieved an AUC of 1.00 for chickenpox, cowpox, and hand-foot-mouth disease (HFMD), indicating excellent discriminative capability for these classes. Meanwhile, the remaining classes, including healthy skin, measles, and monkeypox, achieved AUC values of 0.99 and 0.98, respectively. These results demonstrate that the proposed Hierarchical Swin Transformer model provides highly reliable classification performance across all skin disease categories included in the dataset.
Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation
Concrete surface defect detection is a critical aspect of maintaining the integrity and safety of infrastructure in civil engineering. Traditional manual inspection methods are time-consuming, prone to human subjectivity, and often limited by physical accessibility, necessitating the development of robust automated solutions. This paper presents an automated pixel-level concrete surface defect detection system utilizing the U-Net deep learning architecture. The primary contribution and novelty of our approach lie in optimizing the network's encoder-decoder structure with skip connections to effectively capture both broad contextual features and precise spatial localization. This overcomes the critical limitations of existing traditional methods, which frequently struggle with complex concrete background textures, inherent noise, and uneven illumination. To validate our approach, the proposed U-Net model is systematically compared against a widely used baseline method, K-Means clustering combined with Gray-Level Co-occurrence Matrix (GLCM) texture analysis. The evaluation was conducted using a comprehensive dataset consisting of 1000 high-resolution concrete images. Experimental results reveal that the deep learning architecture vastly outperforms the traditional baseline. Specifically, the U-Net model achieved an outstanding F1-Score of 92.47%, a precision of 93.18%, and a mean Intersection over Union (mIoU) of 86.55%. In stark contrast, the K-Means and GLCM approach only yielded an F1-Score of 69.83% and an mIoU of 54.21%. These quantitative findings demonstrate that the proposed U-Net-based system not only successfully minimizes false segmentations but also provides a highly reliable, efficient, and scalable computational framework. Ultimately, this research delivers a practical solution that can be seamlessly integrated into continuous automated structural health monitoring systems, paving the way for safer and more proactive civil infrastructure management.
Exploring User Acceptance of Chatbot AI: A Triangulated Framework Integrating TAM, ECTM, and TPB Constructs
Artificial intelligence-powered chatbots have revolutionized e-commerce by providing personalized customer interactions, real-time support, and streamlined purchase processes. Despite their widespread adoption, sustained user engagement remains challenging, requiring deeper insights into cognitive, affective, and social determinants of long-term usage. This study addresses this gap by integrating the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Expectation-Confirmation Theory Model (ECTM) into a comprehensive triangulated framework to examine user acceptance and continued purchase intention toward AI chatbots in online shopping. The research investigates direct effects of confirmation, information quality, perceived usefulness (PU), perceived ease of use (PEOU), attitude, and subjective norm on satisfaction, alongside satisfaction's mediating role in predicting continued purchase intention. Data were collected from 504 respondents with prior AI chatbot experience in online shopping via purposive sampling, using validated 6-point Likert scales. Partial least squares structural equation modeling (PLS-SEM) was conducted using SmartPLS 4. Results confirm that confirmation (β=0.178, p=0.037), information quality (β=0.269, p<0.001), PU (β=0.152, p=0.005), PEOU (β=0.235, p<0.001), and attitude (β=0.184, p=0.001) significantly predict satisfaction, which strongly influences continued purchase intention (β=0.868, p<0.001). Subjective norm exhibited no significant effect (β=-0.003, p=0.954). Satisfaction fully mediates ECTM and TAM pathways, underscoring experiential confirmation and system quality's dominance over social influences in post-adoption behavior. Theoretically, this study validates an integrated model advancing post-adoption theory in AI contexts. Practically, findings guide e-commerce platforms to enhance chatbot retention by prioritizing information accuracy, usability, and expectation alignment rather than social norms.
DeepCog: Classification of Mild Cognitive Impairment Using Structural MRI
Early identification of Mild Cognitive Impairment (MCI) is essential for preventing or delaying the progression of severe neurodegenerative disorders. The primary objective of this study is to develop an automated and computationally efficient framework for detecting MCI using structural brain imaging. The proposed research focuses on improving early diagnostic capability through a deep learning–based classification system that analyzes structural changes in brain images. The major contribution of this work lies in combining region-focused morphometric analysis with lightweight convolutional neural network architecture to achieve accurate classification while maintaining computational efficiency suitable for clinical environments. The methodology involves extracting anatomically meaningful features from structural brain scans using a region-of-interest based morphometric approach. Brain images undergo several preprocessing procedures including skull stripping, normalization, spatial alignment and data augmentation to ensure consistency and robustness of the dataset. After preprocessing, the images are used to train a lightweight deep learning model that performs binary classification between cognitively normal subjects and individuals with MCI. The study employs a publicly available neuroimaging dataset consisting of structural brain scans and associated clinical information. Experimental results demonstrate that the proposed framework achieves strong classification performance while maintaining low computational complexity. The model achieves 88.2% subject-wise test accuracy and 0.90 cross-validation accuracy, outperforming commonly used architectures such as VGG16 (78.1%) and ResNet50 (53.7%). These findings indicate that lightweight neural networks combined with region-based anatomical analysis can effectively support automated screening of MCI. The proposed approach has potential implications for scalable clinical decision support systems and may assist neurologists in early diagnosis, timely intervention, and improved cognitive healthcare management. Future research may explore multimodal data integration and longitudinal clinical validation to further enhance diagnostic reliability.
Optimizing Monkeypox Detection Using Advanced Class Imbalance Handling Methods: Smote, Smote-Enn, Smote-Tomek, Borderline-Smote
Monkeypox is a zoonotic viral disease with increasing global concern due to its rapid spread and potential public health impact. Accurate and timely detection is crucial, yet the development of machine learning-based detection systems is often challenged by class imbalance in clinical datasets, leading to biased predictions towards majority classes. This study systematically evaluates the effectiveness of various class imbalance handling techniques, including SMOTE, Borderline-SMOTE, SMOTE-ENN, and SMOTE-Tomek, on the performance of ensemble learning algorithms, specifically Random Forest and Gradient Boosting, for monkeypox detection. Using a dataset of 25,000 synthetic patient records with 11 clinical features, models were trained and validated through stratified 5-fold cross-validation. Performance metrics including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC), along with ROC analysis, were employed to assess the impact of each augmentation method. Results indicate that hybrid methods, particularly SMOTE-ENN, significantly improve recall and F1-score, improving the detection of clinically important monkeypox-positive cases while maintaining adequate discriminative ability. Standard SMOTE and SMOTE-Tomek provide stable performance across metrics, whereas Borderline-SMOTE shows lower recall despite high precision. These findings highlight the importance of selecting appropriate class imbalance handling strategies tailored to the clinical objective, emphasizing sensitivity in detecting positive monkeypox cases. The study provides practical guidance for implementing reliable and robust machine learning models in early monkeypox detection, contributing to improved clinical decision-making and public health interventions.
K-Cube Consensus Clustering with Centroid Improvement and Variance-Based Metrics on High-Dimensional Data
High-dimensional and multidimensional cube data structures (K-Cube) are posing a significant challenge for conventional clustering algorithms due to the effect of dimensionality, uniform feature weight assumptions, and loss of hierarchical information. Therefore, this study aimed to propose K-Cube Consensus Clustering framework, which integrates Variance-Based Centroid Refinement, Weighted Distance Metrics, and consensus voting mechanism to overcome the challenges of high-dimensional cube data. The proposed method systematically clustered all dimensions and sub-dimensions of cube data, refined centroid by emphasizing more stable low-variance attributes, and applied adaptive distance weighting based on variance-derived feature weights integrated into the distance metric to improve cluster assignment. The final clusters were obtained through majority voting of the clustering results for each dimension. Unlike existing consensus clustering methods that operate on flat data representations or combine independent clustering results, the proposed framework explicitly exploits the hierarchical structure of multidimensional cube data by clustering dimensions and sub-dimensions prior to consensus integration. Moreover, variance-based centroid refinement and weighted distance metrics are jointly embedded within each cube dimension rather than applied as isolated enhancements. This hierarchy-aware design preserves cube semantics while simultaneously improving centroid stability and distance adaptivity, resulting in a distinct and scalable clustering framework for complex high-dimensional cube data. The framework processes cube dimensions independently with iterative convergence control, enabling scalable application to large-scale cube data. The results of synthetic and real-world high-dimensional datasets, including cube data with approximately 2.2 million instances, showed that the proposed method consistently outperformed K-Means, K-Medoids, and Hamiltonian formulations. The method produced lower SSE such as 3,179,328 on Arcene and 1,422.21 on Lung Cancer, higher Silhouette Score of approximately 0.5718 and 0.4905 for consensus results, better cluster stability of 0.9947, and faster convergence. These results confirmed the effectiveness of K-Cube Consensus Clustering in producing stable and meaningful clusters in large-scale high-dimensional data applications.
Improvement of Interpolation Performance with Statistical Method in Total Suspended Solid Identification
Total Suspended Solids (TSS) is one of the key parameters used to determine water quality, which can be observed through the density level of suspended particles. The determination of TSS aims to ensure that river pollution levels can be controlled to maintain good environmental quality. However, the identification of TSS is still performed manually, which requires a relatively long processing time. This condition highlights the need for an effective and efficient identification process. Based on these considerations, this study aims to develop an extraction technique to identify TSS in river water using the Interpolation Mean Square (IMS) algorithm. The development of the extraction technique within the IMS algorithm is crucial for improving the performance of linear interpolation methods. Mean Square is proposed as a parameter in the interpolation process to optimize the extraction algorithm. The segmentation process based on the performance of the IMS algorithm involves exploring and grouping image intensity values. The resulting segmented image clusters are subsequently selected based on the values produced by the Mean Square computation, which are then processed as the final segmentation output. The experimental results show an improvement in the performance evaluation results of the IMS algorithm providing an increase of 7% to 10% over the previous linear interpolation method. The evaluation results produced by the IMS algorithm are 90.19% accuracy, 99.99% sensitivity, and 83.33% specificity. These results indicate that the improved interpolation method presented in the IMS algorithm produces optimal results in determining TSS. Improving the performance of the interpolation method through the development of an IMS-based extraction technique has succeeded in producing optimal identification results. The superiority of the IMS algorithm provides novelty in the development of interpolation techniques for automated segmentation. Furthermore, the findings of this study can effectively support the West Sumatra Environmental Agency in addressing river water pollution issues.
Optimizing XGBoost with Optuna for Attendance-Based Prediction of Student Academic Success
The growth of data analytics in higher education has increased the need for predictive models that identify students’ academic potential and support data-driven decision-making. Academic institutions are able to enhance student outcomes by implementing suitable interventions that are based on an accurate early prediction of Grade Point Average (GPA). This study aims to develop a practical and accurate GPA prediction model based on Extreme Gradient Boosting (XGBoost), enhanced through Optuna-based hyperparameter tuning, to support academic monitoring systems in higher education. This model is intended to assist academic monitoring systems in higher education. Using academic performance data, attendance records, and course load information obtained from 961 undergraduate students, a quantitative predictive modeling approach was implemented. The CRISP-DM framework was implemented during the modelling process, which included the following stages: data understanding, data preparation, modelling, evaluation, and deployment. To ensure the stability and relevance of the model, exploratory analysis and correlation assessments were implemented to determine feature selection. Optuna was employed to optimize hyperparameters, utilizing Bayesian optimization with adaptive trial pruning to efficiently examine the parameter space. Experimental results demonstrate that the Optuna-tuned XGBoost model achieved superior predictive performance compared to baseline XGBoost models and models optimized using Grid Search and Random Search. The proposed model attained a coefficient of determination (R²) of 0.8637 and a Root Mean Square Error (RMSE) of 0.1165, indicating improved accuracy and robustness in handling large prediction errors. To enhance practical applicability, the final model was deployed in a Streamlit-based web application that enables real-time GPA prediction and supports academic advisors. Overall, the findings confirm that Optuna-based hyperparameter tuning significantly enhances XGBoost performance and provides a solution for data-driven academic monitoring in higher education institutions.
Monet2Photo: Reverse Style Transfer using CycleGAN with Impressionism-to-Reality Domain
The application of artificial intelligence in artistic image transformation has primarily focused on converting real-world photographs into stylized artworks. In contrast, the inverse task of reconstructing photorealistic images from paintings remains relatively underexplored and presents substantial technical challenges. This study aims to investigate the feasibility and limitations of reverse style transfer by translating impressionist paintings into realistic photographic images, using the works of Claude Monet as a representative case. The main contribution of this research lies in providing a critical examination of reverse image translation under extreme domain gaps, rather than proposing aesthetic enhancement. An unpaired image-to-image translation framework based on CycleGAN is employed to learn mappings between painting and photographic domains without relying on paired data. The methodology is conceptually grounded in adversarial learning combined with cycle consistency constraints to encourage structural preservation while attempting to reconstruct plausible visual features. The experimental setup utilizes a dataset consisting of 300 Monet paintings and 7,028 real photographs, with targeted data augmentation applied to the painting domain to address data imbalance. Prior to model training, exploratory data analysis is conducted to characterize domain discrepancies through visual and statistical comparisons, including color distribution analysis, grayscale intensity patterns, texture descriptors, and dimensionality reduction. Model performance is evaluated through controlled experiments using distribution-based distance measures and qualitative visual inspection. The results indicate that while the model is capable of preserving coarse spatial layouts and generating diverse outputs without memorization, it struggles to recover high-fidelity textures, illumination, and contrast required for photorealistic reconstruction. These findings highlight the inherent limitations of classical CycleGAN architectures for reverse style transfer and suggest the need for more expressive models and stronger constraints in future research on art-to-reality image translation.
Application of Random Data Splitting Technique for Time Series Modeling Using Feedforward Neural Network and Support Vector Regression Models
In predictive modeling, data partitioning mainly involves dividing the dataset into a training set for learning model parameters and a testing set for assessing generalizability through predictive performance. In time series forecasting, non-random data splitting is commonly used where initial observations are used for training, a subsequent portion for validation and latest observations are utilized for testing. However, the effectiveness of random data partitioning where observations are randomly distributed into training, validation and testing subsets remains underexplored within the context of time series data. This study investigates the suitability of random data partitioning in time series forecasting using both stationary and non-stationary simulated datasets, as well as real-world data. Feedforward neural network (FFNN) and Support vector regression (SVR) models were implemented, with model performance optimized through systematic trial-and-error hyperparameter tuning. Under random data-splitting approach, 30 different training and testing subsets are generated to assess the stability and robustness of model performance across different sample compositions. Random data partitioning is implemented at the level of constructed supervised learning instances, where each instance consisted of lagged input variables and their corresponding target values, forming distinct input–output pairs. The allocation of these instances to training and testing subsets is carried out entirely at random, without preserving sequential ordering or grouping temporally adjacent observations, while strictly maintaining original input–output correspondence within each constructed instance. The findings indicate that, for both simulated and real-world datasets, random data splitting resulted in improved predictive performance of both models, yielding lower error metrics compared to non-random splitting. These results suggested that random data splitting can enhance generalization and forecasting performance in time series applications with appropriate models. The study provides valuable empirical evidence on data partitioning strategies, supporting researchers and practitioners in making more informed decisions regarding model evaluation and selection in time series forecasting tasks.
Predicting Whistleblowing Intention Using Supervised Machine Learning: Integrating TPB and IEDM in State-Owned Enterprises
Whistleblowing plays a critical role in detecting organizational misconduct; however, understanding the determinants of whistleblowing intention remains a challenge. Prior studies predominantly rely on regression or structural equation modeling, which focus on explanatory relationships rather than predictive evaluation. This study addresses this limitation by integrating the Theory of Planned Behavior and the Integrated Ethical Decision-Making Model within a supervised machine learning framework. Data were collected from 382 permanent employees of Indonesian state-owned enterprises (BUMN) using a structured questionnaire. Three classification algorithms—Logistic Regression, Support Vector Machine (SVM), and Random Forest—were implemented to evaluate predictive performance. The results indicate that Random Forest achieved the highest predictive accuracy and discrimination capability. Feature importance analysis reveals that perceived behavioral control is the strongest predictor of whistleblowing intention, followed by ethical awareness and attitude, while subjective norms show comparatively weaker influence. These findings refine TPB by demonstrating the dominant role of perceived behavioral control in high-risk ethical decisions and reinforce the importance of ethical awareness as a cognitive trigger within the IEDM framework. The study contributes by bridging behavioral theory and predictive analytics while offering governance insights for strengthening whistleblowing systems in state-owned enterprises.
YOLOv8-Based Microplastic Detection and Quantification in River Water Microscopic Images
Plastic particles with various size variations such as microplastics are environmental contaminants that are widely found in waters and have the potential to cause negative impacts. The process of identifying plastic particles using microscopic imagery manually takes a lot of time and considerable cost. In order to provide an alternative solution as part of early detection, microscopic image-based plastic particle detection was carried out with the YOLOv8 architecture, accompanied by an estimate of microplastic abundance in microplastic units per cubic meter. This study aims to develop and evaluate the detection of plastic particles in microscopic images of river water. This research dataset consists of 300 microscopic images taken from three river locations in Indonesia and annotated for model training and testing. The results of the evaluation showed that the proposed model had an aggregate performance value with a precision value of 0.786, recall of 0.66, and mAP@0.5 of 0.731. Additional test results show that with the addition of image resolution, the precision value can increase to 0.804 and the value mAP@0.5 increases to 0.762, even at the expense of computing time, which is also increasing. Extended scenario-based analysis showed that more than 87% of the detected objects fell into the category of small objects, affecting the localization sensitivity and variability of the estimated MPS value. This study also validated the results of object detection with FTIR-based laboratory tests using a full quantitative agreement between the model detection results and the identification of plastic particle materials at the sampling location level. The main contribution and findings of this study is an integrated evaluation framework for object detection, particle size characterization which is expected to be an alternative to the initial screening tool for plastic particle content.
On the Use of Zero-Inflated Mixed Models for Count Data: A Simulation and Empirical Evidence
This paper evaluates the performance of classical count regression models (Poisson, Negative Binomial, Generalized Poisson), zero-inflated models (Zero-Inflated Poisson/ZIP, Zero-Inflated Negative Binomial/ZINB, Zero-Inflated Generalized Poisson/ZIGP), and zero-inflated mixed models (ZIPMM, ZINBMM, ZIGPMM) for over-dispersed count data, particularly due to excess zeros and unobserved heterogeneity. Using simulation and empirical studies, we evaluated the performance of the models based on their predictive capability and their ability to yield valid inferences through hypothesis testing. The simulation, replicated 1000 times, involves 27 scenarios that combine various sample sizes, proportions of zero counts, and response variable distributions. Our findings indicate that ZIGPMM and ZINBMM provide the smallest root mean square error (RMSE) values. Although the Poisson model yields a relatively small RMSE, it does not adequately account for overdispersion, leading to underestimated standard errors and potentially misleading significance tests. The negative binomial model yields dispersion estimates closest to 1, indicating good performance, whereas ZIGP, ZINB, ZIGPMM, and ZINBMM perform better when zero counts are extremely high. Empirical analysis of data on under-five mortality due to pneumonia in Java Island, Indonesia, indicates that ZINB, ZINBMM, and ZIGPMM have the smallest Akaike Information Criterion (AIC), making them the most suitable models. These models show that exclusive breastfeeding and vitamin A have no significant effect on under-five child mortality due to pneumonia, while severe malnutrition has a statistically significant impact (α=0.05).
Optimized Hybrid CNN-LSTM Model for Predicting Transportation Sector Stock Prices Using Optimizer and Activation Function Tuning
Stock price prediction is inherently complex due to nonlinear dynamics and high volatility, particularly in Indonesia’s transportation sector, which experienced significant inflationary pressure and extreme instability during and after the COVID-19 pandemic. These disruptions introduced structural breaks and regime shifts, intensifying non-stationary market behavior and increasing forecasting uncertainty. Such conditions create an urgent need for robust predictive information systems capable of supporting investment decision-making and risk management in highly volatile environments. However, standalone recurrent models such as Long Short-Term Memory (LSTM) often struggle to capture local micro-patterns and long-term dependencies. Moreover, prior studies have rarely implemented systematic hyperparameter optimization, resulting in inconsistent predictive performance across stocks with heterogeneous volatility. In contrast, Convolutional Neural Networks (CNN) extract local patterns and short-term nonlinear features, making them effective for modeling high-frequency fluctuations. This study proposes a systematically optimized hybrid CNN-LSTM model to forecast transportation sector stock prices using daily OHLC data from 2020-2025. The research framework follows the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, encompassing business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Prior to modeling, preprocessing includes data cleaning, Min-Max normalization, and sliding window transformation to construct supervised learning sequences. CNN is employed to extract localized nonlinear features and reduce noise, while LSTM models long-term temporal dependencies. Model performance is evaluated using MAE, MSE, RMSE, MAPE, and R². Results show that the optimized CNN-LSTM model outperforms the baseline across all stocks. The highest R² of 0.9725 is obtained from one stock, indicating strong performance. In addition, the average R² improves from 0.8736 to 0.9483, an increase of 0.0747 (8.55%). The best results are achieved using ReLU with Adam and Nadam optimizers, demonstrating improved convergence and generalization. These findings highlight the effectiveness of optimized hybrid deep learning models for forecasting in nonlinear and non-stationary financial markets.
Nonintrusive Arrhythmia Detection from Wrist Pulse Using NTSC Color Model in Eulerian Video Magnification
Arrhythmia is a cardiovascular condition characterized by abnormal heart rhythms, such as tachycardia and bradycardia, which may lead to serious health complications if not detected early. This study proposes a non-invasive approach for screening tachycardia by extracting pulse signals from wrist video recordings using Eulerian Video Magnification (EVM) combined with the NTSC color space model. Subtle variations in skin color caused by blood flow, which are typically imperceptible to the human eye, are amplified using the EVM technique to enhance pulse-related motion signals. The NTSC color model is employed to separate luminance and chrominance components (YIQ), allowing more effective identification of pulse-induced color variations in the wrist region. The recorded wrist videos are processed through several stages, including spatial decomposition, temporal filtering, motion magnification, and pixel intensity extraction from the region of interest to obtain a temporal pulse signal. Peak detection is then applied to estimate heart rate in beats per minute (BPM). The performance of the proposed method is evaluated by comparing the estimated BPM values with reference measurements obtained from a Xiaomi Mi Band 2 wearable device. Experimental results based on 20 wrist video recordings demonstrate that the proposed method achieves approximately 96% agreement between the estimated BPM values and the reference measurements. Quantitative evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation analysis further confirms the consistency of the proposed approach. These results indicate that the integration of Eulerian Video Magnification with the NTSC color model has potential as a low-cost and non-contact method for preliminary tachycardia screening and remote cardiovascular monitoring.
PRAKE: A Modified RAKE Model for Keyword Extraction in Accreditation Assessment Descriptions
Study program accreditation requires aligning assessment criteria with the Self-Evaluation Sheet (LED), which is usually written as a lengthy and complex narrative. Finding relevant information requires a method that can automatically extract keywords from assessment descriptions as representations of the criteria. Keyword extraction can be applied through the Rapid Automatic Keyword Extraction (RAKE) method, a simple technique that works without labeled data. However, standard RAKE uses stopwords as delimiters to segment candidate phrases, making it less effective for complex sentences such as those found in accreditation assessment descriptions. Because a single sentence may contain several ideas, the extraction process should handle phrases carefully through splitting, merging, or extension according to their structure and meaning. To address this limitation, this study introduces PRAKE (Phrase-Refined RAKE), a modified RAKE algorithm that enhances candidate phrase extraction. Modifications are carried out at the Candidate Phrase Extraction stage through three techniques, including Phrase Completion to complete short phrases afterwards with the prefix of the previous phrase, Phrase Restructuring to rearrange phrases through merging or separation based on structure and meaning, and Semantic Phrase Composition to form new phrases from different elements that are semantically interrelated. Additionally, a domain term weighting based on term frequency is integrated into the scoring calculation to strengthen the relevance of terms to the accreditation context. The model achieved a precision of 0.90, recall of 0.83, and F1-score of 0.85, representing the average performance across all 101 assessment descriptions evaluated in this study. The results demonstrate that PRAKE adapts better to accreditation terminology and improves keyword relevance and extraction efficiency. These findings indicate that PRAKE provides a foundation for automated evaluation and can be extended for cross-domain document analysis.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image
Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Performance Evaluation of Support Vector Machine (SVM) and XGBoost for Predicting Toddlers’ Stunting Status Based on Anthropometric Data
Stunting remains a primary global health concern, particularly in developing countries, due to its long-term effects on physical growth, cognitive development, and overall well-being. Despite various public health initiatives, challenges in early detection persist, highlighting the need for accurate, data-driven predictive models to support targeted interventions. This study aims to develop and compare the performance of two machine learning algorithms—SVM and Extreme Gradient Boosting (XGBoost)—for classifying stunting status among children under five, in order to determine the most effective method for early prediction. A quantitative machine learning approach was applied to a dataset comprising 17,498 records derived from Posyandu data in Lampung Province, Indonesia. The analytical pipeline included data preprocessing, class rebalancing using the Synthetic Minority Over-sampling Technique (SMOTE), and model evaluation through stratified 10-fold cross-validation. Performance was assessed using accuracy, precision, recall, and F1-score. The XGBoost model demonstrated superior performance with accuracy, precision, recall, and F1-score reaching 0.9979. In comparison, the SVM model produced slightly lower yet still strong results, achieving an accuracy of 0.9949, with similarly consistent performance across other evaluation metrics. These findings indicate that XGBoost more effectively handles high-dimensional, imbalanced data and captures nonlinear patterns in the dataset. XGBoost was identified as the optimal method for stunting classification in this study, outperforming SVM across all evaluation metrics. These results support the integration of boosting-based models into early detection systems for child nutritional assessment. Future studies should incorporate additional environmental and socioeconomic variables and evaluate model applicability in a real-time community health setting.
Enhancing Subjective Career Success Among Private University Academics: The Roles of Perceived Organizational Support, Proactive Personality, and Work Engagement
This study aims to develop and empirically test a conceptual model explaining the determinants of subjective career success among academics in private universities in Indonesia. Drawing on Social Cognitive Career Theory (SCCT) and insights from the Job Demands–Resources (JD-R) theory, proactive personality is conceptualized as a personal resource representing person input and perceived organizational support as a contextual resource that influence career outcomes. In this framework, work engagement is proposed as a key motivational mechanism through which personal and organizational resources are translated into subjective career success. A quantitative research design was employed using a survey method administered to full-time academics at private higher education institutions across Indonesia. Data were collected from 278 respondents and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS version 4. The results indicate that proactive personality and perceived organizational support exert positive and significant effects on subjective career success. Furthermore, work engagement partially mediates the relationships between proactive personality and subjective career success, as well as between perceived organizational support and subjective career success. These findings suggest that the availability of personal and organizational resources plays a critical role in shaping academics’ subjective career experiences. From a theoretical perspective, this study extends the application of SCCT by integrating motivational insights from JD-R theory, demonstrating how personal resources (proactive personality) and contextual supports (perceived organizational support) influence career outcomes through work engagement as a psychological mechanism. Practically, the findings offer insights for private university management in designing faculty development policies that foster work engagement and enhance subjective career success.
Hybrid Machine Learning for Early Prediction of At-Risk Students with Imbalanced Data
The phenomenon of student dropout remains a major challenge for higher education institutions because it impacts academic performance and institutional reputation. Identification of students at risk of dropping out is often hampered by data imbalance, where the number of dropouts is far fewer than active students, so conventional prediction models tend to be biased towards the majority class. This study aims to develop an accurate and reliable prediction framework for students at risk of dropping out to detect at-risk students through a hybrid machine learning approach with data balancing techniques. The main contribution of this study is the integration of Support Vector Machine and Extreme Gradient Boosting in a stacked ensemble architecture supported by data balancing optimization techniques. The proposed model leverages the ability of Support Vector Machine to separate complex classification patterns, while Extreme Gradient Boosting improves prediction accuracy through iterative learning and modeling interactions between variables. The problem of data imbalance is addressed through oversampling techniques for the minority class so that the model learning process becomes more balanced. The model framework is tested using a dataset consisting of 3,652 students with academic, socioeconomic, and behavioral variables. Experimental results show that the proposed hybrid model outperforms the single model, with an accuracy rate of 97 percent, a precision rate of 94 percent, and a recall rate of 95 percent. These findings suggest that a combination of complementary machine learning methods, coupled with data optimization, can significantly improve the predictive ability of student dropout. The practical implication of this research is the availability of a robust decision support system for universities in designing timely and targeted interventions. By identifying students at risk of dropping out, institutions can strengthen retention strategies, improve student academic success, and reduce dropout rates more effectively.
Institutional Readiness and Digital Service Quality as Determinants of Entrepreneur Trust in Risk Based Business Licensing Services
This study examines digital licensing governance in risk-based business licensing services by integrating institutional readiness, digital service quality, service dependability, procedural transparency, public trust, entrepreneur satisfaction, and compliance intention. A quantitative explanatory design was applied using 300 valid responses from entrepreneurs who had used local government licensing services and the Online Single Submission system. The respondent profile shows that 73.0% were micro and small enterprises, 60.7% operated in medium- and high-risk categories, 50.4% used assisted online services at the DPMPTSP office, and 38.3% experienced permit processing durations of more than seven days. The measurement model demonstrated strong validity and reliability, with Composite Reliability values ranging from 0.908 to 0.936 and AVE values ranging from 0.663 to 0.701. The structural model revealed that institutional readiness significantly affected digital service quality and service dependability, while digital service quality strongly influenced service dependability and procedural transparency. Procedural transparency had the strongest direct effect on public trust, with a path coefficient of 0.547, followed by entrepreneur satisfaction toward compliance intention at 0.511 and public trust toward entrepreneur satisfaction at 0.452. Mediation analysis confirmed that digital service quality contributed to satisfaction through service dependability, procedural transparency, and public trust. The findings show that digital licensing reform does not depend solely on system availability, but on the alignment of institutional capacity, digital quality, reliable service delivery, transparent procedures, and public trust. This study contributes a comprehensive governance model for explaining how digital public services strengthen satisfaction and formal compliance among entrepreneurs.