Edumatic: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) is a scientific journal in the field of informatics education published by Universitas Hamzanwadi three times a year, namely in April, August, and December. Since 7 April 2022, the Edumatic: Jurnal Pendidikan Informatika has been accredited with a rank of 3 (SINTA 3) based on the Decree of the Director General of Higher Education Tinggi (SK No. 105/E/KPT/2022). This means that articles published in Vol. 5 No. 2, December 2021 are included in SINTA 3. The focus and scope of this journal are (1) Computers and Informatics in Education, (2) Learning Models and ICT Models, (3) Development of Learning Media Based on Information Technology, (4) Human-Computer Interaction, (5) Information Systems and Information Technology, (6) Data Mining, (7) Internet of Things (IoT), (8) Artificial Intelligence, (9) Algorithms and Numerical Computation, (10) Computers and Networks, and other focuses related to computers and education. All articles in Edumatic: Jurnal Pendidikan Informatika have a Digital Object Identifier (DOI) with the DOI Prefix: 10.29408 by Crossref. In addition, Edumatic: Jurnal Pendidikan Informatika has been indexed by a number of indexing agencies, including GARUDA, Dimensions, Google Scholar, and others. Journal Name Edumatic: Jurnal Pendidikan Informatika Inisial Edumatic Abbreviation Edumatic Publication Schedule The journal will publish three issues per year (April, August, and December) effective from 2025. DOI Prefix 10.29408 e-ISSN 2549-7472 Editor-in-Chief Muhammad Zamroni Uska Publisher Universitas Hamzanwadi Quotation Analysis Google Scholar | SINTA
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X-RayVision-Net: CBAM-Infused YOLOv8 for Rapid Pulmonary Disease Recognition
Tuberculosis and pneumonia continue to pose major global health challenges, particularly in regions with limited radiological resources, where overlapping chest X-ray patterns often complicate differential diagnosis. This study proposes X-RayVision-Net, a hybrid deep learning framework that integrates the Convolutional Block Attention Module (CBAM) into the YOLOv8 architecture to enhance pulmonary disease classification. A quantitative experimental design was employed using 10,056 chest X-ray images categorized as normal, pneumonia, or tuberculosis, collected from multiple public datasets. Image preprocessing involved Contrast Limited Adaptive Histogram Equalization (CLAHE) and balanced data augmentation to improve visual consistency and address class imbalance. The proposed model was trained for 100 epochs and evaluated against a standard YOLOv8 baseline using accuracy, precision, recall, and F1-score. Experimental results demonstrate that the CBAM-enhanced YOLOv8 model achieved an accuracy of 98.99%, outperforming the baseline model (97.37%) and yielding consistent improvements across all performance metrics. The findings confirm that the incorporation of channel and spatial attention mechanisms effectively refines pulmonary feature representation, facilitating more accurate discrimination between tuberculosis and pneumonia. This framework presents a rapid and reliable computer-aided diagnostic approach suitable for deployment in clinical environments with constrained radiology expertise.
Copyright (c) 2026 Louis Mario Wijaya, Daniel Martomanggolo Wonohadihjojo https://creativecommons.org/licenses/by-sa/4.0/
Machine Learning Approaches for Export Trend Classification: Evidence from Leading Commodities in Indonesia
Sorong City holds a strategic position in the export economy of Papua Barat Daya; however, its export performance remains volatile due to global price fluctuations, logistical constraints, and shifts in international demand. To address these challenges, this study applies machine learning-based classification to analyze and predict export trend dynamics of Sorong’s leading commodities. Specifically, the study compares the performance of Naïve Bayes and Random Forest classifiers within a quantitative experimental framework. The dataset comprises 874 export records (2023–2025), including HS Codes, export values, destination countries, exporters, and export types. The methodological workflow encompasses data preprocessing, trend labeling, normalization, label encoding, class balancing using SMOTE, and model evaluation via 80:20 train-test split and 10-fold cross-validation. Performance metrics include accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results reveal that Random Forest outperforms Naïve Bayes, achieving 74% accuracy compared to 57%, and more effectively captures nonlinear feature relationships. Despite a reduction in ROC-AUC during cross-validation, Random Forest demonstrates greater robustness in export trend prediction. Overall, the findings highlight the potential of machine learning to enhance regional trade forecasting, inform evidence-based policy formulation, and strengthen data-driven export management in emerging regional economies.
Copyright (c) 2026 Virasanty Muslimah, Rezki Rezki, Wildan Abdul Jabar https://creativecommons.org/licenses/by-sa/4.0
Revisiting Resampling Strategies under Extreme Class Imbalance: Evidence from Large-Scale Online Payment Fraud Detection
Extreme class imbalance in online payment fraud detection creates an accuracy paradox and an operational risk in which improving fraud capture can generate costly false alarms. This study uses a quantitative, experiment-based design to evaluate the operational impact of common resampling strategies under extreme skew using interpretable linear decision rules. The Online Payments Fraud dataset (6.36 million transactions) from Kaggle is analysed using six monetary balance/amount variables (amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest) plus the rule-based isFlaggedFraud indicator to predict the isFraud label. Five training variants (no resampling, ROS, RUS, SMOTE, ADASYN) are compared with two linear decision rules: an ordinary least squares linear scoring model (thresholded at 0.5) and a linear SVM, using a leakage-free protocol in which resampling is applied only to the 80% training split and performance is assessed on an untouched, highly imbalanced 20% test set. The findings indicate that LinReg–RUS achieves the most balanced operating point (Precision 65.938%, Recall 47.718%, F1 55.367%, ROC-AUC 98.720%), whereas ADASYN increases recall but collapses precision (~2.1%), yielding F1 ≈4.17%. These results contribute controlled, large-scale evidence that under extreme imbalance, simpler resampling–model combinations can provide more deployable precision–recall trade-offs than aggressive synthetic sampling, supporting interpretable baselines for capacity-constrained payment screening.
Copyright (c) 2026 Mursyid Ardiansyah, Ali Asgar Zainal Abidin https://creativecommons.org/licenses/by-sa/4.0/
Feature Interaction and Performance Analysis of RankSum-Based Extractive Summarization in Indonesian Scientific Articles
The extractive summarization of Indonesian scientific articles is hindered by a domain mismatch where established methodologies rely on news-corpus assumptions, whereas Indonesian scientific discourse follows rigid, IMRaD-driven structural and lexical patterns. This study aims to systematically analyze feature interaction effects and saturation behaviour in RankSum-based extractive summaries for Indonesian scientific articles. Designed as a controlled comparative experiment, this research evaluates a RankSum framework integrating variables, such as graph-based, semantic-thematic vectors, and structural heuristics. The dataset comprises 2,897 Indonesian journal articles (2021-2025) collected via web scraping from open-access university repositories. Analysis across 31 scenarios demonstrates that for Indonesian scientific articles, the assumption that increasing feature density improves performance is flawed, instead a feature saturation effect occurs. Results show that a 4-feature combination maximizes unigram lexical precision (ROUGE-1 0.3564), whereas the full 5-feature fusion is necessary to preserve global semantic integrity, structural flow, and stable (ROUGE-L 0.2018; BERTScore 0.6977). This study establishes a generalizable principle for domain-aware ATS by demonstrating that overcoming domain mismatch relies on navigating feature saturation through selection aligned with the document’s inherent logic rather than raw feature quantity.
Copyright (c) 2026 Verrino Adityya, Yohannes Yohannes https://creativecommons.org/licenses/by-sa/4.0/
An Integrated Safety Stock and Net Promoter Score System for Inventory and Customer Loyalty
Manual and separate inventory management and customer loyalty monitoring often lead to information delays, record-keeping errors, low operational efficiency, and an unmonitored relationship between stock availability and customer perception. The aim of our research was to develop a web-based sales and loyalty information system that integrates Safety Stock and Net Promoter Score (NPS) methods into a single decision support framework. Our research is a study of system development using the Waterfall model, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance, supported by use cases and activity diagrams. The findings of this study are an integrated system that is able to calculate minimum stock levels, safety stocks, risk of stock-outs, and display real-time NPS visualizations. The test results obtained through black box testing on system access, inventory processing, and NPS reporting show that all key functions are running well and to specification. The implications of this study suggest that the proposed system can improve inventory accuracy, reduce the risk of stock shortages, improve operational efficiency, and support objective, responsive, and sustainable managerial decision-making for small and medium-sized distributors through an integrated and reliable information system.
Copyright (c) 2026 Arya Putra Badruzzaman, Yudie Irawan, Soni Adiyono https://creativecommons.org/licenses/by-sa/4.0/
Soybean Seed Quality Classification using Magnitude-Enhanced Multiple Channel LBP and SVM
Soybean is a major source of plant-based protein The quality of soybean seed affects resulting food. Therefore, image processing for classifying soybean seed quality is needed. Previous studies mainly used handcrafted or deep learning features and not evaluated local texture representations that using magnitude information for multi-class problems with high visual similarity. Traditional texture descriptors such as LBP or GLCM mainly using sign-based or global statistics and have limitations in representing colour-texture variations. This study aims to classify soybean seed quality using SVM with Multiple Channel Local Binary Pattern (MCLBP) and its enhanced variant with magnitude information (MCLBP+M) for feature extraction by utilizing correlations between colour channels through multi-radius approach. The dataset used is Soybean Seeds includes five classes: intact, spotted, immature, broken, and skin-damaged. This research conduct dataset splitting using 10-fold cross validation, data balancing (SMOTE), feature extraction, SVM model training and testing, and performance evaluation. The results show that MCLBP+M with Lab colour space and RBF kernel achieves accuracy of 86.30%, precision of 86.32%, recall of 85.99%, and F1-score of 86.07%. The results show that magnitude information in MCLBP+M consistently stable and improves classification performance across colour spaces and kernels, making it suitable for soybean seed quality classification.
Copyright (c) 2026 Adrian Chandra, Yohannes Yohannes https://creativecommons.org/licenses/by-sa/4.0/
Mitigating Class Imbalance in Indonesian Sarcasm Detection: A Cross-Platform Transformer Study
Sarcasm detection in Indonesian social media remains challenging due to implicit pragmatic expressions, severe class imbalance, and strong domain variation across platforms. Unlike prior Indonesian sarcasm studies that predominantly focus on in-domain accuracy using conventional balancing methods, this study provides the first systematic cross-platform analysis of generative data balancing under domain shift. We empirically examine whether GPT-4o based generative balancing improves robustness rather than accuracy-centric evaluation in Transformer-based sarcasm detection. Models trained on Twitter data are evaluated across Twitter, Reddit, and TikTok as an unseen domain. The results show that generative balancing yields limited gains in in-domain evaluation but consistently improves cross-domain robustness by increasing sarcasm recall, particularly for Base models. Notably, XLM-R Base achieves an absolute F1-score improvement of +10.8 points on TikTok, while IndoBERT-Large attains the highest in-domain F1-score of 0.7444. These findings indicate that generative augmentation partially mitigates class imbalance by enhancing robustness under domain shift, thereby repositioning sarcasm detection as a robustness-oriented problem and highlighting generative balancing as a complementary strategy rather than a substitute for larger Transformer models in cross-platform NLP settings.
Copyright (c) 2026 A. Salky Maulana, I Made Artha Agastya https://creativecommons.org/licenses/by-sa/4.0/
Beyond Predictive Accuracy: Enhancing Parameter Stability in Multicollinear Time Series Forecasting via Regularisation
Multicollinearity in feature-based time series regression arises as a structural consequence of lagged and rolling feature construction. However, existing studies on Ridge and ElasticNet regularization adopt an accuracy-driven evaluation paradigm, with limited attention to parameter stability, shrinkage behavior, and sensitivity to regularization strength. This study shifts the evaluation of regularized linear models from predictive accuracy toward stability-oriented assessment. Using daily electricity consumption data from the UCI Repository, Linear Regression, Ridge, and ElasticNet models are examined under engineered temporal features derived from stability-based lag pruning, rolling statistics, and correlation-informed feature selection. Model evaluation focuses on bias–variance behavior, coefficient shrinkage, regularization sensitivity, and training–testing performance gaps. The results show that regularization improves stability, with the performance gap decreasing from 0.0961 in Linear Regression to 0.0608 under ElasticNet. These comparisons show that regularization stabilizes regression models via distinct shrinkage mechanisms, informing model selection beyond accuracy. Ridge exhibits conservative shrinkage averaging 6.06%, whereas ElasticNet induces stronger shrinkage averaging 46.32% and shows higher sensitivity to penalty strength. These findings provide methodological evidence that regularization in feature-based time series regression should be treated as a stability strategy rather than an accuracy optimization tool, offering guidance for electricity load forecasting under structurally redundant temporal features.
Copyright (c) 2026 Daffa Kumara Khiar Faisa, Abu Salam https://creativecommons.org/licenses/by-sa/4.0/
Security Maturity Assessment of Indonesian Android Mobile Banking Apps using MobSF and OWASP
The rapid expansion of mobile banking in emerging economies has increased exposure to client-side security risks, while MASVS-based security maturity benchmarking of conventional banking applications remains underrepresented in the literature. This study conducts a standard-based comparative security maturity assessment of two major Indonesian Android banking applications, BRImo and myBCA. APK files obtained from the Google Play Store were analysed using Static Application Security Testing with the Mobile Security Framework (MobSF) and evaluated against OWASP MASVS Level 2 and MASVS-R. MobSF scores were interpreted as relative indicators of security maturity based on severity-weighted findings across multiple domains. The results reveal a clear divergence in maturity levels. Although both applications demonstrate strong network-layer protection, BRImo exhibits structural weaknesses in storage, cryptography, platform interaction, and resilience domains, indicating fragmented defence-in-depth implementation. In contrast, myBCA shows more consistent cross-domain control integration. This study contributes an MASVS-based security maturity benchmarking approach and provides conceptual evidence that formal regulatory compliance may coexist with inconsistent client-side technical implementation. The findings offer analytically transferable insights for developers, security auditors, and regulators in rapidly digitalising financial ecosystems.
Copyright (c) 2026 Rizal Aglal Faozi, Nuur Wachid Abdul Majid, Suprih Widodo https://creativecommons.org/licenses/by-sa/4.0/
Optimizing XGBoost Performance through Recursive Feature Elimination for Methanol Conversion Prediction
The strong nonlinear interaction between catalytic properties and operating conditions complicates accurate space time yield modeling in thermocatalytic carbon dioxide hydrogenation, especially when redundant descriptors are included. Although XGBoost is widely used for predictive tasks, the influence of feature redundancy on generalization and interpretability in carbon dioxide to methanol systems remains insufficiently examined. This study investigates the integration of Recursive Feature Elimination with XGBoost using 639 experimental observations derived from copper based catalysts. Reducing the feature set from fifteen to eight variables improves generalization performance, as indicated by lower prediction error and higher explained variance. The retained variables correspond to key catalytic and operational parameters, including reaction temperature, pressure, and copper content, aligning with established kinetic and mechanistic principles. These results show that eliminating redundant descriptors stabilizes cross validated performance and reduces training complexity without sacrificing predictive accuracy. The reduced model concentrates predictive weight on kinetically relevant variables, providing a clearer quantitative representation of the parameters that govern space time yield in carbon dioxide hydrogenation.
Copyright (c) 2026 Ibnu Richo Kurniawan, Muhamad Febrian Akrom, Novianto Nur Hidayat, Muhammad Naufal https://creativecommons.org/licenses/by-sa/4.0/
An Efficient Two Stage Detection Segmentation Framework for Automated Road Crack Assessment
Road cracks significantly degrade infrastructure quality and pose a threat to traffic safety. To minimize manual inspection inefficiencies, this study investigates a segmentation model integrating MobileNetV3-Small as a backbone for the U-Net architecture to reduce processing time. The performance of the proposed MobileNetV3-Small-U-Net is benchmarked against a standard U-Net using three public datasets: DeepCrack (537 images), CFD (118 images), and Crack500 (3368 images) sourced from GitHub and Kaggle. This research explores the influence of optimization algorithms on evaluation results across these diverse datasets. Specifically, the study evaluates Adam, RMSprop, and SGD optimizers at an image resolution of 224 x 224 pixels, with a 0.001 learning rate and 0.9 momentum. On-the-fly augmentation techniques, including horizontal flips and brightness adjustments (0.8 to 1.2), were implemented during training. Experimental results demonstrate that MobileNetV3-Small-U-Net enhances computational efficiency by achieving a 9 ms inference time, which is 2 ms faster than the standard U-Net. These findings confirm that a MobileNetV3-Small backbone accelerates inference, despite a slight trade-off in evaluation metrics. Additionally, results reveal that the SGD optimizer is unsuitable for these segmentation tasks due to high error rates and the lack of an adaptive learning rate.
Copyright (c) 2026 Alvin Hujaya, Muhammad Rizky Pribadi https://creativecommons.org/licenses/by-sa/4.0/
Mapping Digital Sentiment Landscapes of Hotel Reviews: A Machine Learning-Based Cross-Platform Analysis
The expansion of online travel agencies (OTAs) has produced large volumes of user-generated hotel reviews, offering important resources for sentiment analysis of consumer perceptions. However, prior studies largely rely on single-platform datasets and focus on classification performance, with limited attention to cross-platform sentiment consistency and the impact of data imbalance. This study aims to analyse and compare sentiment patterns across Traveloka, Tiket.com, and Accor, while evaluating a machine learning framework under imbalanced data conditions. This study adopts a quantitative experimental design using 3,000 Indonesian-language reviews collected via web scraping. The independent variable is reviewing text, and the dependent variable is sentiment classification (positive/negative). Data were preprocessed and transformed using TF-IDF, and classified using Multinomial Naïve Bayes, with performance evaluated by accuracy, precision, recall, and F1-score. The results show that positive sentiment consistently dominates across all platforms, with Accor achieving the highest performance, followed by Tiket.com and Traveloka. However, very high recall values for the positive class indicate substantial class imbalance, which biases predictions and reduces sensitivity to negative sentiment. This study provides empirical evidence of cross-platform sentiment consistency and highlights the importance of addressing data imbalance in sentiment modelling.
Copyright (c) 2026 Muhammad Kholid Ridwan, Yudie Irawan, Raden Rhoedy Setiawan https://creativecommons.org/licenses/by-sa/4.0/
Citizen-Centric Decision Support Systems for Housing Assistance: A Low-Barrier E-Government Approach
Digital public services often require account-based authentication that can create access barriers for citizens with limited digital literacy. In housing assistance services, such barriers may reduce the accessibility of systems intended to support vulnerable groups. This study aimed to develop and evaluate a low-barrier decision support system for housing assistance by integrating Guest Mode access with the Simple Additive Weighting (SAW) method. The study used a design-oriented development approach in collaboration with Dinas Perkim Minahasa. System requirements were identified through staff interviews, and the prototype was tested in the agency environment using real housing data. Validation was conducted by comparing system-generated SAW results with manual calculations performed by agency staff and by testing the main system functions operationally. The results showed that the system produced the same ranking results as the manual calculations in the tested cases, while staff evaluation indicated that the Guest Mode design simplified the submission process and reduced administrative barriers. This study contributes to inclusive e-government practice by proposing a citizen-centered, low-barrier architecture for housing assistance services that maintains decision support accuracy while improving service accessibility.
Copyright (c) 2026 Ben Shalom Manopo, Audy Aldrin Kenap, Kristofel Santa https://creativecommons.org/licenses/by-sa/4.0/
On the Effectiveness of Lightweight CNN Architectures for Fine-Grained Coffee Bean Classification
Distinguishing coffee bean varieties remains a significant challenge in the agricultural industry due to high inter-class similarity and the subtle morphological differences between species. This study aims to conduct a comparative evaluation of MobileNetV2 and EfficientNetB0 for fine-grained coffee bean classification, specifically investigating how efficiency-oriented architectural mechanisms such as depthwise separable convolution and compound scaling influence feature extraction. The research employed a quantitative experimental method using a private dataset of 2,400 images comprising Arabica, Robusta, and Liberica varieties. Data preprocessing included resizing to 224×224 pixels and augmentation, followed by training the two architectures using transfer learning under a controlled experimental framework. The results showed that EfficientNetB0 achieved superior performance with a testing accuracy of 99.17%, while MobileNetV2 attained a competitive accuracy of 98.33% with lower computational complexity. These results demonstrate that while EfficientNetB0 is optimal for high-precision industrial sorting, MobileNetV2 offers a highly efficient alternative for resource-constrained mobile applications. This study provides a scalable framework for automating quality control, effectively balancing architectural efficiency with the sensitivity required for accurate coffee variety identification.
Copyright (c) 2026 Akbar Muhamad Burhanudhin, Usman Sudibyo, Eka Putra Agus Meindiawan https://creativecommons.org/licenses/by-sa/4.0/
Mobile Learning Design and Its Role in Fostering Critical Thinking in Informatics Education
The use of mobile learning in vocational education continues to grow; however, its implementation often emphasizes technological development without sufficient attention to instructional organization and learning evaluation. This study aims to develop a Kodular-based mobile learning application and examine its practical feasibility and user acceptance in informatics learning. The research employed a research and development approach with exploratory evaluation, involving 20 vocational high school students selected through purposive sampling. Data were collected using user response questionnaires and learning indicator instruments to capture students’ perceptions of usability, learning support, and cognitive engagement after using the application. The collected data were analyzed using descriptive percentage analysis to illustrate trends related to media feasibility and user acceptance. The results show that all evaluated aspects achieved percentages above 85%, indicating a very good level of practical feasibility and user acceptance, particularly in terms of usability, ease of use, and perceived learning support. These findings suggest that the developed mobile learning application can function as a supportive learning medium in vocational informatics education. This study provides practical insights into the development of mobile learning media that aligns instructional organization with learner needs in vocational learning contexts.
Copyright (c) 2026 Rizky Gabriella Sandy, Arico Ayani Suparto, Dyan Yuliana https://creativecommons.org/licenses/by-sa/4.0/
Mutual Information-Driven Feature Selection for Efficient DDoS Detection Using Modern Boosting Ensembles
Distributed Denial of Service (DDoS) attacks generate high-dimensional network traffic that poses significant challenges for machine learning-based detection systems in terms of predictive accuracy and computational efficiency. This study presents a systematic evaluation of Mutual Information (MI) based feature selection applied to three modern boosting algorithms, namely XGBoost, LightGBM, and CatBoost, using the CIC-DDoS2019 dataset. A controlled experimental design was employed, where data partitioning was performed prior to resampling, and SMOTE was applied exclusively to the training set to prevent data leakage. Feature selection was conducted by identifying the top 25 features based on MI score saturation analysis. The results demonstrate that MI-based feature selection consistently improves classification performance while substantially reducing training time across all models. Among the evaluated methods, LightGBM achieves the best trade-off between accuracy and computational efficiency, reaching an accuracy of 99.88% with significantly reduced training cost. These findings indicate that feature quality plays a critical role in shaping the learning behaviour of boosting algorithms and that MI-based feature selection functions as a structural mechanism for enhancing model stability and scalability in high-dimensional DDoS detection scenarios.
Copyright (c) 2026 Muhammad Febrian Fansuri, Kusrini Kusrini https://creativecommons.org/licenses/by-sa/4.0/
Behaviorally Interpretable Transactional Features for Customer Segmentation Using K-Means in Grocery Retail
Customer segmentation based on transactional data is widely used to understand purchasing behavior in retail. However, many existing studies tend to emphasize algorithm performance, with limited discussion on how transactional variables represent actual customer behavior. This study adopts a quantitative approach using transactional sales data from a grocery retail store (Toko Solo Latri), consisting of 10,000 item-level records collected during June 2025. The analysis follows the CRISP-DM framework, covering data understanding, preparation, modeling, and evaluation stages. Customer behavior is represented through several aggregated variables, including transaction frequency, total items purchased, and product diversity. The K-Means clustering algorithm is applied to group customers into meaningful segments. The number of clusters is determined using the Elbow Method and further evaluated using Silhouette analysis. The results reveal three distinct customer segments with different levels of purchase intensity and product diversity. The Silhouette Score of 0.464 indicates a moderate clustering structure. In addition, one-way ANOVA shows significant differences across the observed variables, with large effect sizes (η² ranging from 0.736 to 0.822). These findings suggest that constructing behavior-based transactional features can improve the interpretability of customer segmentation results.
Copyright (c) 2026 Rendy Muhammad Aprizal, Oktalia Kumala Sari, Arif Bramantoro https://creativecommons.org/licenses/by-sa/4.0/
Advancing Sustainability Education through Narrative Digital Media: A Structural Model of Student Acceptance
While teaching the 17 Sustainable Development Goals (SDGs) is important, many schools struggle to keep students interested, often leading to boredom or confusion. This research evaluates a 2D visual novel, Liora Island, for enhancing engagement and reducing cognitive complexity when teaching the SDGs. Using a quantitative associative design and the Technology Acceptance Model (TAM), five variables Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual Use (AU) were examined among 139 students at Mondial Senior High School, Batam. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal high student acceptance across all TAM constructs. Perceived ease of use emerged as the primary driver of adoption, strongly influencing perceived usefulness and attitude. Attitude toward using significantly predicted behavioral intention, which in turn strongly determined actual use. Perceived usefulness showed smaller but significant effects on attitude and behavioral intention, indicating that usability outweighs instructional value during initial acceptance. This study contributes to sustainability pedagogy by identifying usability as key in reducing "sustainability fatigue." Practically, the results indicate that user friendly, narrative driven media are superior to traditional text heavy methods for communicating complex global challenges in secondary education.
Copyright (c) 2026 Deli Deli, William William, Clara Audelia, Bayu Syahputra https://creativecommons.org/licenses/by-sa/4.0/
Integrated Behavioral Perceptual Evaluation for Comparative E-Commerce Web Usability: Evidence from Shopee and Tokopedia
The growth of e-commerce has intensified competition among digital platforms, making usability a crucial factor in user engagement and convenience. However, many usability studies rely on single-method evaluation approaches, rarely conduct cross-platform comparisons, and seldom examine the relationship between perceived usability and behavioral interaction efficiency. This study compared the web usability of Shopee and Tokopedia by combining subjective usability assessment with eye-tracking analysis. Thirty-nine participants completed search, filter, and cart tasks on both platforms while gaze data were recorded and then completed the USE Questionnaire. The data were analyzed using non-parametric tests and AOI-based eye-tracking metrics. The results showed that both platforms achieved comparable perceived usability and received a “Good” classification. However, eye-tracking findings revealed task-dependent differences in visual attention, particularly during search activities. These findings demonstrate that perceived usability equivalence does not necessarily indicate behavioral interaction efficiency. This study contributes methodologically by proposing an integrated evaluation framework that combines biometric and psychometric measures, and theoretically by providing empirical evidence of the divergence between subjective perceptions and objective behavioral efficiency, with implications for improving visual hierarchy and navigation in e-commerce interfaces.
Copyright (c) 2026 Mifta Fauzia Rahma, Chanifah Indah Ratnasari https://creativecommons.org/licenses/by-sa/4.0/
Utilitarian vs Human-Centered AI Acceptance: Explaining Students’ Adoption of ChatGPT in Higher Education
The growing use of generative artificial intelligence (AI) in higher education raises questions about how students assess and adopt these systems, particularly whether traditional utilitarian models are sufficient to explain their use. This study compares the Technology Acceptance Model (TAM) and the Human-Centered AI Acceptance Model (HCAIAM) in explaining students’ behavioral intention to use ChatGPT, while examining how functional and human-centered factors operate within the same framework. A cross-sectional design was used, involving 100 undergraduate students in Indonesia selected through convenience sampling, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that TAM provides stronger explanatory power and better model fit (R² = 0.765; SRMR = 0.073) than HCAIAM (R² = 0.709; SRMR = 0.136). Perceived usefulness and perceived ease of use emerge as the main drivers of intention, indicating that students tend to use ChatGPT primarily as a tool to support academic tasks. In contrast, human-centered factors such as transparency and ethical alignment influence intention indirectly through trust and attitude. The autonomy construct shows weak reliability and overlaps with other variables, suggesting limitations in its measurement. These findings indicate that utilitarian factors remain central in this context, while human-centered aspects play a more conditional role, and point to a layered pattern of AI acceptance in which different types of factors operate at different levels.
Copyright (c) 2026 Dwi Angesti Dinda Parameswara, Berlilana, Rujianto Eko Saputro https://creativecommons.org/licenses/by-sa/4.0/
Compliance Without Experience? A Task-Based WCAG 2.1 Evaluation of Mobile Banking in Indonesia
Mobile banking applications play a central role in digital financial systems; however, accessibility for users with visual impairments remains insufficiently examined in real task contexts. Existing studies primarily emphasize automated and interface-level assessments, with limited attention to interaction processes during task execution. This study evaluates the accessibility of a widely used mobile banking application in Indonesia using a task-based approach based on WCAG 2.1 Level A and AA criteria. An exploratory descriptive case study was conducted on BRImo (version 2.92.0) using Android 13 and TalkBack 16.2.0. The evaluation integrates manual WCAG inspection, automated testing, and screen reader-based interaction testing across three core tasks: login authentication, balance inquiry, and fund transfer. The unit of analysis focuses on interaction steps within task workflows, examined through a triangulation approach comparing structural compliance and interaction-level outcomes. The findings show substantial partial conformance, with over 70% of Level A and nearly 90% of Level AA criteria categorized as “Supports with Exceptions.” However, critical barriers persist in transactional workflows, including missing labels, inconsistent screen reader feedback, and navigation failures that hinder task completion. These results indicate that WCAG conformance alone is insufficient to ensure functional accessibility, highlighting the distinction between structural conformance and experiential task accessibility.
Copyright (c) 2026 Fatin Maetam, Chanifah Indah Ratnasari https://creativecommons.org/licenses/by-sa/4.0/
Portable IoT Traffic Light System with Deterministic Cycle Recalculation and Real-Time Remote Configuration
Traffic control in temporary or emergency situations often requires portable traffic signal systems. However, many existing traffic light systems are designed as fixed infrastructure and limiting portability. This study proposes a portable IoT-based traffic light system with real-time remote configuration and a deterministic cycle recalculation mechanism to ensure coordinated signal operation. The system is implemented using ESP8266 microcontrollers, a web server with database integration, and WebSocket-based real-time communication. Performance evaluation measured synchronization accuracy and network communication quality using Round Trip Time (RTT), one-way delay, and Received Signal Strength Indicator (RSSI). The results show consistent cycle synchronization across all test scenarios, with no observed deviation between the expected and actual cycle durations. Outdoor communication testing demonstrated stable connectivity up to approximately 70 meters, with average RTT increasing from 33.44 ms to 95.44 ms while remaining within acceptable limits for real-time synchronization before degradation beyond 75 meters and connection loss at 80 meters. This study highlights the integration of portability, deterministic synchronization, and real-time configuration within a single system design. The proposed approach offers a flexible and cost-efficient alternative for temporary traffic management applications.
Copyright (c) 2026 Nada Tiyasa Salsabela, Tsania Camila Finnisa, Sendi Novianto https://creativecommons.org/licenses/by-sa/4.0/
Beyond Traditional Models: Integrating EUCS, SERVQUAL, and UEQ in SeaBank
Research on digital banking satisfaction remains conceptually fragmented, as End-User Computing Satisfaction (EUCS), SERVQUAL, and User Experience Questionnaire (UEQ) reflect distinct evaluative paradigms that often produce inconsistent results when applied independently. This study develops and validates an integrated model capturing informational, service, system, and experiential determinants of satisfaction in a branchless digital banking context. Data from 212 active SeaBank users were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a Two-Stage approach. The model explains 76.5% of the variance in user satisfaction. User experience is the strongest predictor, followed by system quality and information quality, while service quality is not statistically significant. The findings indicate an experience-centric evaluation structure, where affective and system-related factors outweigh traditional service dimensions. This study provides a unified empirical validation of EUCS, SERVQUAL, and UEQ, clarifying their hierarchical relationships and indicating the reduced relevance of service quality in fully digital environments. Practically, the results highlight the importance of UI/UX optimization, system reliability, and experiential design for enhancing user retention. The Gen Z-dominated sample and cross-sectional design limit broader generalization, suggesting the need for longitudinal and cross-context validation.
Copyright (c) 2026 Raihana Nabilatulrahmah, Dinda Lestarini, Iin Seprina, Ari Wedhasmara, Dinna Yunika Hardiyanti https://creativecommons.org/licenses/by-sa/4.0/
Gamified Mobile Learning for Teaching the Prophet’s Mandatory Traits in Elementary Education
The integration of digital media in Islamic elementary education remains limited, particularly in studies that integrate Islamic character education with game-based learning and provide quantitative evidence of learning effectiveness. This study develops and evaluates the usability of an Android-based educational game, Kisah Teladan Rasul, designed to support elementary school students’ understanding of the Prophet’s mandatory traits (ṣidq, amānah, tablīgh, and faṭānah) through gamified mobile learning. A Research and Development approach using the ADDIE model was employed, involving 19 fourth-grade students at SDN Margorejo 2 Pati. The small sample size reflects the exploratory and developmental nature of this study, which prioritizes systematic media development over broad generalizability. Learning improvement was examined through a one-group pretest–posttest design yielding an average N-Gain score of 0.69 (medium category), indicating meaningful improvement in conceptual understanding. Usability was assessed using the System Usability Scale, with a mean score of 90.1, categorized as Excellent (Highly Acceptable). These findings suggest that an Android-based educational game integrating narrative missions, scenario-based challenges, and immediate feedback can serve as effective complementary media in Islamic elementary education. This study contributes by combining systematic instructional design (ADDIE), game-based mobile learning, and empirical usability evaluation within the specific context of Islamic character education.
Copyright (c) 2026 Sakti Chandra Hanafi, Chanifah Indah Ratnasari https://creativecommons.org/licenses/by-sa/4.0/
Evaluation of PCC Load Balancing for Dual ISP Networks: Enhancing Throughput and Traffic Stability
Multi-ISP network management often experiences imbalanced traffic distribution and decreased connection stability despite having adequate bandwidth capacity. Previous studies indicate that load balancing methods such as PCC are effective in traffic distribution; however, they remain limited to implementation aspects, with constraints in analyzing network processing efficiency and inconsistencies in results across various testing conditions. This study aims to empirically evaluate the effectiveness of PCC integrated with failover in improving dual ISP network performance. It employs a controlled experimental design with a comparative approach before and after implementation in a real network environment. The measured parameters include throughput, traffic distribution, connection stability, and packet rate, which are analyzed using a quantitative measurement-based comparative approach. The results show an increase in download throughput from 15–18 Mbps to 36–41 Mbps (±130–150%) and upload throughput from 14–17 Mbps to 36–40 Mbps (±120–140%), more balanced traffic distribution, and a significant reduction in packet rate, indicating improved network processing efficiency. The integration of failover also ensures service continuity without significant disruption. This study provides empirical evidence that a connection-based load balancing approach not only improves traffic distribution but also enhances network processing efficiency, extends previous research, and is relevant for multi-ISP networks with dynamic traffic.
Copyright (c) 2026 Bustomi Bustomi, Muhlis Tahir https://creativecommons.org/licenses/by-sa/4.0/
Deep Learning for PM2.5 Prediction under Zero-Inflated Tropical Rainfall: RNN vs GRU
Air quality forecasting in maritime tropical regions is challenged by zero-inflated rainfall regimes, where prolonged dry periods are intermittently disrupted by extreme precipitation, generating highly non-linear PM₂.₅ dynamics and limiting the effectiveness of conventional predictive models. This study evaluate the predictive performance of Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) under such distributional conditions. A quantitative experimental design with a comparative approach is employed using 1,461 daily observations from the Central Java Climatology Station, incorporating rainfall, temperature, and relative humidity as predictors; a chronological data split preserves temporal dependencies, and performance is assessed using MAE, RMSE, MAPE, and R² metrics. The results indicate that GRU achieves only a marginal 4.3% reduction in MAE relative to RNN, while both models exhibit substantial predictive failure, as evidenced by negative R² values and MAPE exceeding 300%, with predictions collapsing toward the mean and failing to capture extreme pollution events. These findings demonstrate that standard recurrent architectures with conventional loss functions are intrinsically limited in modeling zero-inflated environmental data, contributing empirical evidence on the boundary conditions of deep learning in tropical air quality forecasting and underscoring the necessity for specialized modeling approaches to support reliable early warning systems.
Copyright (c) 2025 Ikhsan Yuliyono, Aji Supriyanto https://creativecommons.org/licenses/by-sa/4.0/
Algorithm-Sensitive Feature Weighting using Mutual Information and ReliefF for Heart Disease Classification
Heart disease remains a critical global health issue requiring reliable early prediction for clinical decision-making. This study evaluates the effect of feature selection and feature weighting on the performance of machine learning models with different learning mechanisms, namely K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Logistic Regression (LR), for heart disease prediction. An algorithm-sensitive comparative framework was applied using global feature relevance estimation (Mutual Information), local feature relevance estimation (ReliefF), feature selection, and feature weighting, with raw features as baseline. Experiments were conducted on the Cleveland Heart Disease dataset using stratified 5-fold cross-validation. The results show that ReliefF-based feature weighting achieves the best performance for Naïve Bayes (accuracy = 0.8085; F1-score = 0.7774), while Logistic Regression attains the highest overall performance under the baseline (accuracy = 0.8547; F1-score = 0.8324). Feature selection improves KNN performance due to reduced sensitivity to irrelevant features. These findings indicate that the effectiveness of feature importance strategies depends on model-specific learning behavior, where feature weighting benefits probabilistic models, while feature selection is more effective for distance-based models. This study contributes an algorithm-sensitive evaluation perspective for aligning feature importance strategies with machine learning model characteristics to improve heart disease prediction performance.
Copyright (c) 2026 Dwi Prihantono, Umar Faqih, Indra Indra https://creativecommons.org/licenses/by-sa/4.0/
Forward Chaining Expert System for Optimizing Marketing Strategies in Social Commerce Platforms
The complexity of digital performance indicators in social commerce environments poses significant challenges for small and medium enterprises (SMEs) in formulating coherent and actionable marketing strategies. This study develops and evaluates a forward chaining based expert system to support structured, data driven, and interpretable marketing decision-making. A design science research methodology was employed, encompassing problem identification, artifact development, and evaluation. Knowledge was elicited through literature synthesis, expert consultation, and empirical observation, and subsequently formalized into IF–THEN production rules within a structured knowledge base. The system applies a forward chaining inference mechanism to process key indicators, including followers, engagement rate, promotion frequency, and conversion rate, in order to generate prioritized strategic recommendations. Evaluation was conducted using scenario-based testing and expert validation to assess accuracy, consistency, and contextual appropriateness. The results demonstrate complete alignment between system outputs and expert judgment across all evaluation scenarios, indicating high reliability and logical consistency of the rule-based reasoning process. The system also produces context-sensitive and interpretable recommendations aligned with varying levels of business performance. This study contributes by advancing rule-based decision support systems in social commerce and providing an explainable and practically applicable tool to enhance marketing decision quality among SMEs.
Copyright (c) 2026 Andhika Rudiansyah, Novi Rukhviyanti https://creativecommons.org/licenses/by-sa/4.0/
Demand-Based Product Classification Using K-Means with Intermittency Metrics
Inventory management at multi-SKU distribution companies becomes complex when most products have unstable and intermittent demand patterns. At PT JJA, procurement is still reactive without the use of historical patterns, while the previous approach generally relied on aggregate indicators such as average sales so that it has not been able to comprehensively capture temporal dynamics. This study aims to group products based on temporal demand patterns using K-Means Clustering in 11,988 transactions for the 2020–2025 period which are processed into 261 products through monthly aggregation, with features of average sales, coefficient of variation (CV), zero_month_ratio, Average Demand Interval (ADI), and trends. The results showed four optimal clusters (k = 4) with a Silhouette Score of 0.62 and an unbalanced distribution, where one cluster dominated 240 products. The values of zero_month_ratio (>0.80), ADI up to >12 months, and CV up to >3.5 show intermittent demand patterns and long-tail structures. The study confirms that the integration of temporal features (ADI, zero_month_ratio, CV, and trend) transforms the representation of demand from static aggregates to dynamic structures, while linking segmentation results with more adaptive procurement strategies to reduce the risk of overstock and understock.
Copyright (c) 2026 Ariska Nur Anggraini, Amali Amali, Muhammad Syaibani Anwar https://creativecommons.org/licenses/by-sa/4.0/
Hybrid Human AI SDLC for Rapid SaaS Development: Evidence from a 60 Days Case Study
There is a vacuum in the risk of architectural changes in critical systems because macro-architectural governance in AI-based software development is frequently ignored in current scholarly debate. The purpose of this study is to assess how well the Visualize, Integrate, Build, Execute (VIBE) architecture addresses the stability-speed contradiction in SaaS development. This study triangulated data from 465 automated CI/CD pipeline logs, 124 AI instruction tactic documentation records, and 42 test cases using comparative performance analysis and process tracking using an explanatory mixed-methods case study methodology on a stock market analytics platform. The study's key conclusions show a 50% boost in development efficiency, reducing a 60-day cycle to 30 days while preserving system reliability with an average latency of 1.2 seconds and a 99.9 percent availability rate. Specialist synergy was identified where humans became the primary cognitive players in architectural design at 90 percent, and AI as the executor of basic syntax at 85 percent. The research concludes that the architectural anchoring mechanism by humans is crucial for mitigating the risks of non-deterministic AI outputs. Theoretically, this study introduces the concept of human-AI cognitive alignment, while practically providing a validated roadmap for modernization of sensitive infrastructure such as Electronic Medical Records.
Copyright (c) 2026 Muhammad Hilmy Haidar Fawwazie, Nathan Daud, Muhammad Iqbal Rabani, Budi Fajar Supriyanto https://creativecommons.org/licenses/by-sa/4.0/