Journal of Information Systems and Informatics
Journal of Information System and Informatics
Asosiasi Doktor Sistem Informasi Indonesia
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A Meta-Synthesis of Ethics-Aware Software Engineering Practice: A Preliminary Framework
The use of evidence-based practice to inform ethical software development is important for all stakeholders to achieve successful software and address the ethical needs of those affected by it. Although research exists on various mechanisms for supporting ethical software development, their integration into a framework that enables expansive ethics awareness and adherence is lacking. This study fills this gap by conducting a qualitative metasynthesis of studies that implemented and evaluated mechanisms to support ethics in software development. Meta-ethnography methodology was followed and supported by the PRISMA reporting guidelines to achieve the study's objective. Most of the mechanisms identified in the four selected studies are oriented toward supporting the integration of ethics into software development in the artificial intelligence domain, rather than the broader software engineering ethics spectrum. The study contributes a conceptual framework for ethics-aware software engineering practice. The proposed framework requires empirical validation. In addition, Further studies are required to explore other resources reporting on other transformative technologies to expand the framework for improving the software engineering practice.
Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
An Explainable AHP with Sensitivity Analysis Approach for Strategic IT Project Prioritization in Higher Education
This study addresses the strategic challenges faced by private universities in Indonesia in selecting the most suitable IT project to support digital transformation, comparing a Next-Generation Learning Management System (LMS) and an Integrated University Mobile Super-App. Rather than developing a new method, this study enhances the conventional Analytical Hierarchy Process (AHP) by integrating sensitivity analysis and priority decomposition to improve decision explainability (Explainable MCDM). Expert assessments were collected from five academic stakeholders using pairwise comparisons based on Saaty's scale. Four criteria were assessed: Strategic Alignment, Benefits & Impact, Feasibility & Resources, and Risk Assessment. The results show a narrow preference for the LMS (50.7%) over the Super-App (49.3%), primarily driven by its superiority on the highest-weighted criterion, Strategic Alignment (35.6%), particularly the "Support for Core Educational Goals" sub-criterion. Sensitivity analysis reveals that the ranking would reverse if the Benefits & Impact weight increases from 0.200 to above 0.278. This study provides a transparent, replicable, explainable AHP framework that enables decision-makers to understand not only what is recommended but also why and under what conditions the recommendation changes.
Modeling Generative AI Adoption in Higher Education: The Role of Interface Quality, Algorithmic Transparency, and Trust in Human-AI Interaction
This study examines generative artificial intelligence (AI) adoption in higher education by integrating interface quality, algorithmic transparency, and trust within a Human-AI Interaction framework. The study addresses limitations of traditional technology acceptance models, which often overlook psychological and relational factors in AI-enabled environments. Data were collected through a cross-sectional survey of 195 respondents, including students, lecturers, and administrative staff from higher education institutions in West Nusa Tenggara, Indonesia, within a cross-sectional regional sample, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Trust in AI is the strongest predictor of Behavioral Intention (β = 0.623, p < 0.001), followed by Perceived Usefulness (β = 0.270, p < 0.05). Interface Quality significantly affects Perceived Ease of Use (β = 0.803), while Algorithmic Transparency strongly influences Perceived Control (β = 0.824) and Perceived Usefulness (β = 0.562). AI Anxiety was not found to have a significant direct or moderating effect. The model demonstrates substantial explanatory power (R² = 0.710) and strong predictive relevance. This study proposes an integrated dual-path model combining cognitive and affective mechanisms to explain generative AI adoption in higher education. The findings emphasize that AI systems should be designed not only for functionality, but also for trust, transparency, and user confidence.
Two-Stage Tuning of Machine Learning Models for Heart Disease Classification on Synthetic Data
Heart disease remains a leading global cause of mortality, highlighting the need for accurate early risk classification. This study benchmarks Random Forest, XGBoost, and Logistic Regression for heart disease risk classification using a synthetic, perfectly balanced dataset, while addressing performance limitations caused by inadequate hyperparameter configuration. The dataset comprised 70,000 samples with a 50/50 class distribution and 18 clinical and demographic features. Although useful for controlled benchmarking, synthetic balanced data may yield optimistic estimates and may not fully represent real-world clinical variability. Each model was implemented in a scikit-learn Pipeline with median imputation and, where applicable, standard scaling. A two-stage tuning strategy was applied by combining RandomizedSearchCV with GridSearchCV refinement to optimize model configurations systematically. Under these benchmarking conditions, XGBoost achieved the best test performance, with an F1-score of 99.34%, AUC-ROC of 99.97%, and accuracy of 99.34%. Random Forest obtained an F1-score of 99.20% and AUC-ROC of 99.95%, while Logistic Regression achieved an F1-score of 99.12% and AUC-ROC of 99.95%. Age, pain in the arms/jaw/back, and cold sweats/nausea were the most influential predictors. The proposed framework is reproducible, computationally efficient, and suitable for validation on heterogeneous clinical datasets.
Heart Disease Risk Classification; Two-Stage Hyperparameter Tuning; Machine Learning; Comparative Analysis; Feature Importance
Empirical CPU–Memory Benchmarking for Long-Read Genome Assembly Resource Optimization in High-Performance Computing
Efficient resource utilization is a critical challenge in High-Performance Computing (HPC) environments, particularly for long-read genome assembly workflows that require substantial computational resources. This study presents an empirical benchmarking framework to optimize resource allocation for de novo long-read genome assembly of Acacia crassicarpa. Nine experimental scenarios were evaluated by varying CPU cores (32, 48, and 64) and memory allocations (32 GB, 64 GB, and 128 GB) managed via the Slurm workload manager. Performance was assessed based on execution time, assembly continuity (N50), and biological completeness using BUSCO. The results demonstrate that CPU scalability significantly impacts performance, reducing execution time by up to 49% when scaling from 32 to 64 cores. Conversely, increasing memory allocation beyond 64 GB yielded no significant improvements in assembly quality, highlighting the risks of resource over-provisioning. Scenario 2 (64 CPU cores and 64 GB RAM) was selected as the optimal configuration because it balanced runtime, N50 continuity, memory efficiency, and BUSCO completeness, not because it produced the absolute shortest runtime. Under Scenario 2, the workflow achieved an average runtime of 59 hours 39 minutes 40 seconds, an N50 value of 7.8 Mb, and a genome completeness score of 99.8%. These findings provide practical guidance for resource planning and workload scheduling in shared HPC-based genomic workflows.
Mapping Sentiment Analysis in Educational Technology: OpenAlex Bibliometrics, Thematic Trends, and Research Gaps (2013-2025)
This study aims to map the intellectual structure, publication growth, collaboration patterns, thematic evolution, and research gaps of sentiment analysis in educational technology. The study addresses the lack of an open and integrated bibliometric synthesis that connects productivity, collaboration, topic modelling, and gap detection in this field. A bibliometric and science-mapping approach was applied using OpenAlex-indexed publications from 2013 to 2025. After deduplication and eligibility screening, 977 publications were analysed, while 768 papers with sufficient abstract text were used for Non-negative Matrix Factorisation topic modelling. The analysis included publication trend analysis, country and institutional productivity, co-authorship networks, keyword burst analysis, geographic gap analysis, and platform mention analysis. The results show that annual publications increased from 22 papers in 2013 to 123 papers in 2024, with India and China as the most productive countries. Six thematic clusters were identified: Learning Analytics, Social Media Sentiment, Emotion Recognition, MOOCs and E-learning, Transformers/LLMs, and ML Classifier Ensembles. Learning Analytics was the largest cluster, while Transformers/LLMs showed the fastest recent growth. The novelty of this study lies in its reproducible OpenAlex-based bibliometric framework, which integrates performance analysis, science mapping, thematic evolution, and research gap identification for sentiment analysis in educational technology.
Key Scalability Effects on Entropy and Computational Complexity in a GA-SA Hybrid Cryptosystem
Digital data security demands robust encryption systems in which key randomness quality serves as the primary determining factor. Metaheuristic algorithms such as the Genetic Algorithm (GA) and Simulated Annealing (SA) exhibit significant potential for key generation optimization. However, each is individually susceptible to premature convergence and slow computational time, respectively, motivating their sequential hybridization. This study proposes a GA-SA hybrid cryptographic architecture with dynamic population sizing to optimize pseudo-random keystream generation in XOR encryption, evaluated using 15 PDF document datasets across three key configurations: 16 characters (128-bit), 32 characters (256-bit), and 64 characters (512-bit). The hybrid system consistently reduced local optima entrapment across all configurations, with the 64-character key achieving the highest randomness quality at a Shannon Entropy of 7.9288 bits/byte and a mean NIST SP 800-22 Monobit Frequency Test P-Value of 0.2999, though this does not constitute a full NIST SP 800-22 suite evaluation. Runtime analysis showed near-linear empirical growth within the tested range, from 0.0361 seconds to 0.1305 seconds, without exponential bottleneck effects, suggesting the proposed architecture is a promising candidate for pseudo-random keystream generation under tested conditions, with further validation recommended before production deployment.
CSF and IT-BSC Integration in IS/IT Strategic Management: A Conceptual Model from a Systematic Literature Review
In the era of digital transformation, aligning Information Systems and Information Technology (IS/IT) strategies with organizational objectives remains a critical challenge in strategic management. Previous studies have examined Critical Success Factors (CSFs) in Strategic Information Systems Planning (SISP) and the IT Balanced Scorecard (IT-BSC) as a performance measurement framework. However, these approaches are often discussed separately, while their integration within a unified IS/IT strategic management perspective remains limited. This study conducts a Systematic Literature Review (SLR) using the PRISMA approach to examine the roles of CSFs and IT-BSC in IS/IT strategic management. A total of 27 articles published between 2020 and January 2026 were selected from multiple academic databases. The findings show that CSFs primarily support the identification and prioritization of IS/IT strategic initiatives through the SISP process, while IT-BSC is widely used to measure performance, assess alignment, and support structured strategic improvement. The review further indicates that CSFs and IT-BSC should be understood as complementary components. Based on the literature synthesis, this study proposes a conceptual CSF and IT-BSC integration model that links strategic objectives, CSFs, IS/IT needs, IT-BSC perspectives, and performance evaluation. The proposed model is literature derived and has not yet been empirically validated. Therefore, it should be understood as a conceptual basis for future empirical testing and further development in IS/IT strategic management.
User Satisfaction with the Semeton LH Chatbot: An Integrated TAM and DeLone-McLean Approach
Digital transformation in public services has encouraged the adoption of chatbots as effective and efficient tools for communication and information delivery. The Central Lombok Regency Environmental Agency developed the Semeton LH chatbot to improve public access to environmental service information. This study aims to analyze user satisfaction with the Semeton LH chatbot by integrating the Technology Acceptance Model (TAM) and the DeLone and McLean Information Systems Success Model. A quantitative approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, based on data collected from 96 respondents. The measurement model evaluation indicates that several indicators met validity and reliability criteria; however, some constructs still showed limitations in convergent and discriminant validity, particularly in the Average Variance Extracted (AVE) and Heterotrait-Monotrait Ratio (HTMT) values. Therefore, the structural findings should be interpreted cautiously. The results show that Attitude Toward Using (ATU) has a positive and significant effect on Behavioral Intention to Use (BI) and User Satisfaction (US). In addition, Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) significantly influence ATU. Overall, user satisfaction is primarily shaped by positive attitudes formed through perceived ease of use and perceived usefulness.
DeLone and McLean Information Systems Success Model
Development and Evaluation of a Multi-Algorithm Application for Predicting Breast Cancer Patient Survival
This study developed a multi-algorithm machine learning prototype for multiclass breast cancer survival prediction using 1,980 patient records, classifying patients as Living, Died of Disease, or Died of Other Causes. The framework integrated NN, SVM, RF, NB, and KNN algorithms within a decision-support monitoring application, with preprocessing steps including data cleaning, normalization, feature preparation, and dataset partitioning. To prevent target leakage, survival-related variables were excluded from the predictor set. The revised evaluation results indicated that NB and KNN delivered the strongest performance, achieving weighted average F1-scores of 0.93 and 0.92, respectively, while NN and RF showed comparatively lower results. These findings highlight the potential of machine learning for breast cancer survival status monitoring, although the proposed system is designed as a decision-support prototype rather than a clinical diagnostic tool. Therefore, before actual healthcare deployment, more research incorporating explainable AI techniques, external validation, and real-world clinical testing is needed.
A Systematic Literature Review of Dissolved Oxygen and Turbidity Monitoring in Biofloc Aquaculture: IoT and Machine Learning for Water Quality Management
Various technologies have been developed to monitor dissolved oxygen (DO) and turbidity in aquaculture, yet integrated evaluations focusing on biofloc systems, particularly those involving IoT and Machine Learning (ML), remain limited. This review analyzed 32 studies published between 2020 and 2026 using the PRISMA 2020 framework to examine DO measurement, turbidity measurement, IoT integration, and ML applications in biofloc aquaculture. To support methodological discussion, several studies from broader aquaculture and water-quality monitoring contexts were also considered. The reviewed literatures shows that IoT-based and manual methods are the most commonly used approaches for DO and turbidity monitoring. IoT systems, mainly based on ESP32, ESP8266, and Arduino platforms, support real-time monitoring and automation. ML models such as Random Forest, LSTM, and CNN-LSTM are frequently applied for water-quality prediction, anomaly detection, and decision support. However, challenges related to sensor calibration, data availability, and model generalization remain. These findings suggest a growing shift toward more intelligent and integrated aquaculture monitoring systems.
Customer Segmentation in an Internet Service Provider: A K-Means Case Study of Telecommunication Company
PT Lintas Jaringan Nusantara, an internet service provider, faces challenges in utilizing customer data, which is mainly used for administrative purposes such as billing and support, limiting deeper analysis. This study applies K-Means clustering under the CRISP-DM framework for customer segmentation-based service-oriented attributes: internet package, price, and NAS location, using 972 customer records. Categorical attributes were transformed using frequency encoding and manual mapping. Model evaluation using the Elbow Method suggested 3 clusters, while the Silhouette Coefficient indicated that 10 clusters were optimal, improving the score from 0.5471 to 0.7704. The resulting clusters show variations in customer characteristics and provide an exploratory overview of grouping patterns. However, the 10 clusters solution should not yet operationally validated, as stakeholder validation involving marketing and customer service teams is still required to assess interpretability, business relevance, and practical applicability. Further validation using additional customer data or alternative datasets is also recommended. Overall, the findings serve as an initial analytical step to support future data-driven decision-making.
An Empirical Evaluation of Confidence Miscalibration in Vanilla BERT-Based Stress Detection on Social Media
This study evaluates the reliability of confidence estimates produced by a Vanilla BERT classifier for stress detection using the Dreaddit benchmark. BERT-base-uncased was fine-tuned on 3,553 labeled text segments, following the standard split of 2,838 training samples and 715 test samples. The model was assessed as a single diagnostic baseline without additional linguistic features, label smoothing, post-hoc calibration, or other calibration interventions. Evaluation was conducted using discriminative performance metrics, including accuracy, precision, recall, and F1-score, as well as probabilistic reliability metrics, including Brier Score, Expected Calibration Error, Adaptive Calibration Error, and a reliability diagram. The Vanilla BERT model achieved 79.02% accuracy, 78.00% precision, 82.65% recall, and 80.26% F1-score, indicating competitive classification performance for stress detection. However, the calibration results revealed noticeable miscalibration, with a Brier Score of 0.1565, Expected Calibration Error of 0.0847, and Adaptive Calibration Error of 0.0880. The most prominent confidence mismatch occurred in the 0.8–0.9 confidence interval, while the 0.9–1.0 interval contributed the most to Expected Calibration Error due to its larger sample proportion. These findings show that although Vanilla BERT performs reasonably well in distinguishing stressed from non-stressed text, its confidence estimates are not fully reliable. Therefore, this study positions Vanilla BERT as a diagnostic reliability baseline and emphasizes the importance of evaluating stress detection models using both classification performance and probabilistic calibration criteria.
Hypertension Classification Using Correlation-Based Feature Selection (CFS) with Random Forest, XGBoost, and Support Vector Machine: A Comparative Study on Indonesian Hospital Data
Hypertension is a major global health problem that significantly contributes to cardiovascular disease and mortality. This study evaluates the performance of Random Forest, XGBoost, and Support Vector Machine (SVM) algorithms integrated with Correlation-Based Feature Selection (CFS) for hypertension classification using hospital clinical data. The dataset comprises 500 clinical records containing demographic and physiological variables. CFS was applied to reduce irrelevant and redundant attributes before model training. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC through 10-fold cross-validation. Statistical significance was examined using the Friedman test followed by the Wilcoxon signed-rank test with Bonferroni correction. The results show that CFS improved classification performance across all models by approximately 5–6%. XGBoost achieved the best performance with 93.5% accuracy and 0.96 AUC, followed by Random Forest and SVM. However, systolic and diastolic blood pressure, which define the hypertension label, were retained as predictors, indicating a diagnostic classification design rather than independent risk prediction. Therefore, the findings should be interpreted as dataset-based hypertension classification, not future hypertension risk prediction.
ROI-Based Shape-Prior Reconstruction for YOLOv8n-seg-Based Fetal Cerebellum Ultrasound Segmentation
Fetal cerebellum segmentation in ultrasound images is important for quantitative analysis of fetal brain development, yet it remains challenging due to speckle noise, low contrast, acoustic artifacts, and unstable anatomical boundaries. This study proposes an ROI-Based Shape-Prior Reconstruction method as a post-processing refinement stage for YOLOv8n-seg fetal cerebellum segmentation. A total of 294 fetal ultrasound images with manually annotated binary cerebellum masks were used and divided into training, validation, and testing subsets using a 70:20:10 ratio. YOLOv8n-seg generated the initial segmentation masks, while the proposed ROI-based reconstruction stage refined the foreground region using a convolutional autoencoder trained on ROI-based binary cerebellum masks. Compared with raw YOLOv8n-seg, the proposed method improved DSC from 0.9282 to 0.9302 and IoU from 0.8671 to 0.8708. Boundary performance also improved, with HD95 decreasing from 15.06 to 14.18 and ASSD decreasing from 5.38 to 5.20. Although these improvements were modest and not statistically significant, the proposed method produced smoother boundaries and more morphologically consistent segmentation outputs in the visual evaluation. These results indicate that ROI-based shape-prior reconstruction can serve as a lightweight refinement stage for improving boundary consistency in fetal cerebellum ultrasound segmentation. However, external validation with larger datasets is still required to assess generalization.
PCOS Classification Using Random Forest, Recursive Feature Elimination, and Explainable AI
Ovary Syndrome (PCOS) is an endocrine-related condition predominantly affecting women during their childbearing years who experience delayed diagnosis due to the limitations of conventional methods that require laboratory tests and imaging procedures that are relatively costly and time-consuming. This study develops a PCOS classification model based on a clinical dataset of 541 patients with 42 clinical attributes using the random forest algorithm with Recursive Feature Elimination (RFE) feature selection and an Explainable AI (XAI) approach. The research pipeline comprised several sequential stages: problem identification, data collection, preprocessing, data splitting, feature selection, model training and testing, evaluation, and SHAP-based explainability analysis. Performance was evaluated using Accuracy, Precision, Recall, and F1-score, and compared between two models, namely RF+CF and RF+RFE, where RF+RFE was identified as the best-performing model. The XAI approach using SHAP (SHapley Additive exPlanations) was applied to identify and explain the contribution of clinical variables to the classification results. The best model, RF+RFE, achieved an accuracy of 92.66%, precision of 93.75%, recall of 83.33%, and F1-score of 88.24%, demonstrating superior performance compared to RF+CF. As this study relies on a single dataset, broader validation across multiple centers is recommended before clinical deployment. This model is intended as a screening-support approach and has not been validated as a clinical diagnostic tool. The findings are anticipated to serve as a foundation for building data-driven early screening tools and clinical decision-making support systems.
Modeling Student Learning Profiles from LMS Behavioral Traces Using Big Data Analytics
Digital learning environments and Learning Management Systems (LMSs) generate large volumes of time-stamped behavioral traces that can be used to examine how students access resources, navigate course structures, communicate, and approach assessments. Traditional learning-style models often depend on static self-report categories and may not reflect how students actually study in digital courses. This study develops a learning analytics framework for modeling student learning profiles from authentic LMS behavioral traces. The study used a quantitative, non-experimental, longitudinal design based on Canvas LMS interaction data from 15,342 undergraduate students enrolled in 150 large-enrollment courses during the 2023–2024 academic year. More than 500 million raw interaction logs were processed into 24 engineered behavioral features representing temporal engagement, resource access, navigation behavior, interaction activity, and assessment timing. After feature normalization, K-Means clustering was applied, and the optimal cluster solution was selected using the elbow method and average silhouette score. Cluster distinctiveness was examined using one-way analysis of variance, and the association between cluster membership and academic performance category was evaluated using a Chi-squared test. The analysis supported a four-cluster solution. Assessment procrastination and navigation sequentially were the strongest differentiating features.
Designing Smart-Contract-Enabled Liquidity Management for Wholesale Rupiah Digital: A Design Science Research Approach
This study proposes a smart-contract-enabled liquidity management reference architecture for Bank Indonesia's Wholesale Rupiah Digital (wRD), addressing a gap in Project Garuda where the Proof-of-Concept adopts a gross settlement model supplemented by decentralized queueing but lacks a liquidity-saving mechanism. Using Design Science Research (DSR), a Multivocal Literature Review (MLR) was conducted to synthesize eight academic and grey literature sources that include central bank reports, BIS publications, and industry white papers. Five key takeaways were extracted and mapped to architectural modules. The resulting architecture operates on a unified ledger and comprises five modules: a Policy & Criteria smart contract for participant eligibility and compliance, a centralized Queue smart contract with configurable release rules, a Liquidity-Saving Mechanism (LSM) for periodic multilateral netting, an Automated Liquidity Provisioning (ALP) module for just-in-time intraday repo against tokenized bonds when netting is insufficient, and a guarded Automated Market Maker (AMM) for cross-currency settlement. The architecture is illustrated through a payment state lifecycle with two scenario transaction walkthroughs and assessed through a criteria-based analytical evaluation that verifies design completeness against requirements. Empirical validation through prototyping, simulation, or expert review is left for future work. The main contribution is a literature-derived reference architecture that integrates global CBDC mechanisms adapted to Indonesia's context.
Sentiment Analysis of Google Maps Reviews on Temple Tourism in Central Java Using IndoBERT Embeddings and BiLSTM
The rapid growth of user-generated content provides valuable insights into tourists’ perceptions of destinations. This study analyzes sentiment in Google Maps reviews of temple tourism destinations in Central Java using IndoBERT embeddings and a Bidirectional Long Short-Term Memory (BiLSTM) model. A total of 10,714 Indonesian-language reviews were collected through web scraping and processed through preprocessing, pseudo-labeling, embedding generation, and model training. To prevent data leakage, the dataset was divided into stratified training and testing sets, while Random OverSampling (ROS) was applied only to the training data. Since manually annotated labels were unavailable, sentiment categories were generated automatically using a pre-trained IndoBERT classifier. The BiLSTM model achieved 80.25% accuracy on the imbalanced dataset and approximately 95% accuracy against IndoBERT-generated pseudo-labels under balanced training conditions. Improvements in Macro F1-score and balanced accuracy indicate better recognition of minority classes. However, the results should be interpreted cautiously because pseudo-labeling and oversampling may affect performance. Overall, this exploratory study demonstrates the potential of IndoBERT and BiLSTM for Indonesian tourism sentiment analysis while highlighting the need for human-annotated data and stronger validation in future research.
Integrating ISO, CIPP and CMMI Frameworks for Data Privacy Compliance: A System-Level Maturity Assessment with PDCA-Based Architecture in a KBMI Group IV Bank
This study examines the organizational and technical readiness of a systemically important Indonesian bank (KBMI Group IV) in responding to the enactment of the Personal Data Protection (PDP) Law, which necessitates robust privacy engineering and system architecture adaptation. This maturity assessment is conducted as a single-case study based on empirical data collected from two primary respondents: a Business Branch Manager and a Department Head Application Developer. To comprehensively evaluate the system, this study integrates a multi-model framework. First, the Context, Input, Process and Product (CIPP) model qualitatively measures the organization's governance, resources, workflows and policy impacts. These qualitative findings are then translated into CMMI-based process maturity scores. The observed empirical findings reveal an overall maturity score of 3.69, positioning the organization at Level 3 (Defined) as an institutional baseline rather than a sector-wide indicator. The observed findings also expose a regulatory conflict between the PDP Law's 'Right to be Forgotten' and mandatory financial data retention regulations. To address these observed gaps, the study proposes two framework outputs: a comprehensive mapping matrix that aligns regulatory requirements with ISO 27001 and ISO 27701 standards and a conceptual PDCA-based system architecture utilizing data masking and pseudonymization. Although the proposed framework is developed within the context of a single institution, it offers a valuable preliminary foundation for evaluating technical privacy compliance in the banking sector, subject to further validation across a broader range of financial institutions.
Lung X-ray Image Classification for Distinguishing Tuberculosis and Pneumonia Using Pretrained CNN Feature Extractors and Supervised Classifiers
Tuberculosis (TB) and pneumonia (PNA) are infectious lung diseases with overlapping chest X-ray (CXR) manifestations, making automated differential classification clinically important and methodologically challenging. This study proposes a supervised CXR classification workflow to distinguish TB from PNA using pretrained convolutional neural network (CNN) feature extractors and supervised classifiers. A publicly available de-identified dataset comprising 390 TB and 390 PNA images was used. Images were screened to exclude duplicates, corrupted files, non-CXR images, unclear labels, and identifiable cases. Preprocessing included format standardization, resizing according to CNN input requirements, and normalization. To reduce augmentation-based leakage risk, no heavy pre-validation augmentation was applied. Image embeddings were extracted using VGG-16, Inception V3, and VGG-19, then classified using Logistic Regression, Support Vector Machine, and Neural Network models. Performance was evaluated using stratified 5-fold cross-validation with AUC, accuracy, F1-score, precision, recall, MCC, and confusion matrix analysis. The Inception V3–Logistic Regression combination achieved the best performance, with AUC of 0.999, accuracy of 0.992, F1-score of 0.992, and MCC of 0.985.
An Empirical Comparison of C4.5, Naive Bayes, and KNN for Scholarship Selection
Scholarship selection is a critical process in higher education that requires objective, fair, and efficient evaluation of applicants based on academic and socio-economic criteria. However, manual assessment methods are often vulnerable to bias, inconsistency, and administrative inefficiencies, which may affect the transparency and quality of decision-making. This study compares the performance of three supervised machine learning algorithms—C4.5 Decision Tree, Naive Bayes, and K-Nearest Neighbor (KNN)—for scholarship recipient classification. The dataset consisted of 1,500 student records obtained from the KelasAI repository and included ten predictor attributes, namely Grade Point Average, Parental Income, Academic Semester, Family Dependents, Organizational Involvement, Academic Achievement, Regional Origin, Scholarship Type, National Examination Score, and Economic Status. The target variable was categorized into Accepted and Rejected classes. Experiments were conducted using RapidMiner Studio with 10-fold stratified cross-validation to ensure reliable model evaluation. The results showed that Naive Bayes achieved the best performance, with 81.6% accuracy, 81.8% precision, and 81.3% recall, outperforming C4.5 and KNN. These findings demonstrate the potential of machine learning to support more transparent and data-driven scholarship selection processes.
Conceptual Design of a Permissioned Blockchain-Based Asset Management System in Higher Education using Hyperledger Fabric
Effective asset management is a fundamental pillar of higher education operations and strategic sustainability. However, existing conventional systems are often hampered by systemic challenges, including operational inefficiencies, lack of transparency, and chronic data integrity issues resulting from fragmented information silos. This study proposes a conceptual design for a higher education asset management system utilizing blockchain technology to address these governance weaknesses. Based on a comprehensive literature review and system architecture modeling, this study synthesizes concepts from asset management, blockchain principles, and enterprise platforms without deploying a functional prototype. The proposed conceptual framework is built on a permissioned blockchain platform, specifically Hyperledger Fabric, and adopts a multi-layered system architecture for modularity. To balance data integrity with storage efficiency, the system conceptually applies a hybrid data storage pattern combining on-chain ledger records and off-chain databases. Furthermore, the business logic is modeled into smart contracts (chaincode) to automate and secure all stages of the asset lifecycle, from procurement to disposal. The study proposes a theoretical blueprint designed to provide a single, immutable source of truth and enforce multi-level digital approvals. The impact of this research offers a conceptual architectural blueprint that aims to shift academic asset management from a reactive administrative task to an automated, transparent governance ecosystem, paving the way for future validation tasks including performance, scalability, usability, and organizational feasibility.
Enterprise Architecture Design for the Electronic-Based Government System (SPBE) at the East Kalimantan Housing and Settlement Office Using the TOGAF ADM Framework
The Electronic-Based Government System (SPBE) is a central component of Indonesia's digital governance strategy. At the East Kalimantan Housing and Settlement Office (Disperkim), the quality index of residential areas has persistently failed to reach its annual target, indicating fragmented information system use and structural inefficiencies in managing infrastructure, facilities, and public utilities (PSU) services. This study proposes an enterprise architecture design based on the TOGAF ADM framework to address this gap. Employing an architectural design research approach, the study collected primary data through semi-structured interviews with five Disperkim stakeholders and secondary data from Presidential Regulation No. 95 of 2018 on SPBE. The resulting blueprint covers four domains, business, data, application, and technology, and is aligned with organizational performance indicators and regulatory requirements. A key contribution is the proposed integration of the Sakti Gemas mobile application as a centralized public service platform that is expected to support more responsive PSU service delivery. The proposed architecture was validated at the design level through a Focused Group Discussion (FGD) with key Disperkim stakeholders, confirming its contextual appropriateness and technical feasibility. No implementation or quantitative service-performance evaluation has been conducted; operational impact can only be measured after system deployment. This study contributes a replicable architectural model for improving digital governance effectiveness in similar regional government institutions.
ESRGAN-Enhanced YOLOv12 for Rice Leaf Disease Detection with Dataset Partitioning Analysis
Rice leaf diseases pose a significant threat to agricultural productivity, yet accurate automated detection remains challenging due to low image quality in field conditions. This study proposes the integration of ESRGAN-based super-resolution with YOLOv12 for rice leaf disease detection using a dataset of 6,204 annotated field images spanning five classes: bacterial leaf blight, brown spot, healthy leaves, hispa, and leaf blast. To prevent data leakage, all images were partitioned into training, validation, and testing subsets prior to augmentation; under the S3 scenario (75:10:15), the training set was expanded from 4,653 to 13,959 images through augmentation. ESRGAN RRDB 4× enhancement was applied exclusively to all test-set images, enabling a clean before-and-after comparison without contaminating training data. The primary finding is that ESRGAN produces a modest but consistent improvement in detection performance: mAP@0.5 increased from 0.949 to 0.955, and mAP@0.5:0.95 increased from 0.910 to 0.925. Per-class analysis shows the largest gains in visually challenging classes, particularly Leaf Blast (+0.073) and Hispa (+0.060). Additionally, four dataset partitioning scenarios (S1–S4) were trained and evaluated under identical settings; as a preliminary observation, the S3 configuration offered a balanced trade-off between training data availability and test-set reliability, though definitive conclusions require further validation through repeated experimental runs.
A Hybrid SEM-PLS and ANN Approach for Predicting Student Loyalty in Higher Education Learning Management Systems
This study aims to develop a hybrid Structural Equation Modeling–Partial Least Squares (SEM-PLS) and Artificial Neural Network (ANN) approach to analyze student loyalty in Learning Management Systems (LMS) at ISB Atma Luhur. Data were collected from 200 students at ISB Atma Luhur, representing a single-institution sample, and analyzed using SEM-PLS to examine causal relationships and ANN (Multilayer Perceptron) implemented in SPSS to support predictive analysis. The model includes e-service quality, user experience, information quality, and system quality as predictors of satisfaction and loyalty. The SEM-PLS results show that E-Service Quality (β = 0.350), System Quality (β = 0.170), and User Experience (β = 0.292) significantly affect Satisfaction, whereas Information Quality is not statistically significant (p = 0.054). Satisfaction positively influences Loyalty (β = 0.360), and User Experience has the strongest direct effect on Loyalty (β = 0.484). The model explains a substantial proportion of variance (R² = 0.717 and 0.631) with positive Q² values (0.460 and 0.379). Across ten independent runs, the ANN model achieved an average accuracy of 84.88% (SD = 2.82) and an average AUC of 0.949 (SD = 0.003), indicating stable predictive performance, indicating promising predictive performance under the current testing configuration. The findings provide context-specific explanatory and predictive insights into student loyalty in LMS, however, they should be interpreted with caution due to discriminant-validity limitations and the single-institution setting of the study.
Multi-Seed Robustness Benchmark of Lightweight YOLO Models for Young Crescent Moon Detection under Limited-Data Conditions
Visual observation of the young crescent moon is challenging due to its thin and low-contrast appearance. Although YOLO-based object detectors are promising for image-based crescent localization, newer architectures do not automatically generalize well to small grayscale datasets, and prior studies rarely report robustness across repeated training runs. This study benchmarks YOLOv8n, YOLO11n, and YOLO26n for young crescent moon detection under limited-data conditions. A grayscale dataset of 697 images was resized to 640 × 640 pixels, annotated with the single class crescent_moon, and split into training, validation, and test subsets at a fixed 70:20:10 ratio. The three models were trained using the same configuration across five random seeds. Validation results were used to analyze multi-seed robustness, while the fixed 71-image test set was used for CPU-only inference evaluation. YOLO26n achieved the highest validation mAP@50-95 and fitness with the lowest variability, and also achieved the lowest CPU pipeline latency and highest throughput on the test set. These findings show that YOLO26n offers the best trade-off between accuracy and efficiency across the evaluated dataset and CPU-only inference setting. The reported throughput reflects low-frame-rate image-based inference, not real-time video performance. This study provides a reproducible benchmark protocol that combines fixed data splitting, grayscale preprocessing, data integrity checking, multi-seed robustness analysis, and CPU inference profiling.
A 1D-CNN Model with Modified MITDB-SVDB Dataset for Multiclass Arrhythmia Classification
Automated arrhythmia classification from electrocardiogram (ECG) signals remains challenging because public datasets are highly imbalanced and fine-grained multiclass performance may degrade when labels are mapped to the clinically standardized AAMI EC57 grouping scheme. This study proposes real-record dataset enrichment combined with a compact one-dimensional convolutional neural network (1D-CNN) for both fine-grained and AAMI-grouped beat classification. Fourteen records from the MIT-BIH Supraventricular Arrhythmia Database were inserted into the MIT-BIH Arrhythmia Database, adding 4,649 S beats, 4,530 V beats, and 47 Q beats without synthetic oversampling. Preprocessing included Christov R-peak segmentation, beat extraction, per-beat min-max normalization, and resampling to 180 Hz. The 1D-CNN was evaluated under 16-class, 17-class, and 5-class AAMI EC57 schemes. Using ASGD, the model achieved accuracies of 99.10%, 98.58%, and 99.38%, with macro F1-scores of 0.90, 0.87, and 0.97, respectively. Cross-database testing on INCARTDB reached 99.13% accuracy across four mappable classes (N, V, R, A), indicating limited 4-class transferability rather than full AAMI generalization. The approach preserves authentic ECG morphology while addressing minority-class scarcity. The findings show that real-beat enrichment can improve balanced ECG classification, although results are based on beat-level random splits and require future record-wise validation before clinical deployment.
Does It Matter If the Content Is Generated by Generative AI? A Rapid Literature Review of Academic Integrity, Ethics, and Pedagogical Implications
The current wave of adoption and use of generative artificial intelligence (GenAI) across various human sectors, particularly in higher education, has prompted debate and discussion about its applications and ethical implications in higher education. This paper presents insight into the question and ongoing debate: Does it matter if the content is generated or written by GenAI? To contribute to ongoing debates and discussions, this article employed a rapid literature review research methodology to examine literature articles within the timeframe of 2024 to March 2025, involving 59 scientific papers across databases such as Scopus, ResearchGate, and Web of Science, as well as grey literature on Google Scholar. The methodology enabled the researcher to review past technological evolution, draw inferences, and link it to the current wave of AI and GenAI in higher education. The paper found that the evolution of computers and technology has raised many questions, tech stress issues, and discussions, which remain worrisome even to this day. The study found that the ongoing debate and discussions on the impact and ethical implications of GenAI in higher education still confuse both academics and non-academics about the future of GenAI. However, the document provided answers to the ongoing question and highlighted some recommendations for the effective application of GenAI in higher education institutions (HEIs).
AI and Blockchain Adoption in E-Government 3.0: A Systematic Review and Conceptual Framework
This study presents a systematic review of the adoption of Artificial Intelligence (AI) and Blockchain technologies in E-Government 3.0 within developing countries, based on 99 reviewed studies published between 2020 and 2025. Using the PRISMA 2020 protocol, the review synthesizes existing literature to identify key barriers, opportunities, and emerging governance patterns. The findings suggest that while AI appears to enhance decision-making capabilities, and blockchain is associated with enhanced transparency and data integrity, their successful adoption appears to be constrained by institutional capacity, regulatory frameworks, organizational readiness, and digital trust. The results further indicate that technological factors alone are insufficient without alignment with governance structures and policy environments. Based on the synthesis, this study proposes the SIF-G3.0 conceptual framework, which integrates technological, institutional, regulatory, and trust dimensions as a conceptual model for understanding digital government transformation. The proposed SIF-G3.0 framework remains conceptual and has not yet been empirically validated. The evidence base included heterogeneous sources, including empirical, conceptual, bibliometric, and systematic review studies. The study also highlights critical research gaps and provides directions for future research in hybrid intelligent governance systems.
Embracing Augmented Reality and Virtual Reality in South African Basic Education: A Systematic Literature Review
This study reviews the integration of augmented reality (AR) and virtual reality (VR) in South African basic education. To investigate how immersive technologies are applied in teaching and learning, focusing on learner outcomes, teacher preparedness and systemic enabling conditions. The systematic literature review (SLR) was guided by Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Systematic Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) frameworks and analysed 22 studies from journal articles, conference papers and theses published between 2018 and 30 September 2025. AR and VR adoption remains uneven and largely experimental, with implementation concentrated in urban and better-resourced facilities, particularly within STEM-related subjects. Browser-based simulations, low-cost virtual laboratories and mobile applications were the most frequently reported technologies, while fully immersive VR deployments were limited due to the financial and infrastructural constraints. Also, immersive technologies can improve experiential learning, conceptual understanding and learner engagement. Adoption is constrained by policy limitations, inadequate infrastructure, unequal access between rural and urban schools and insufficient teacher training. Synthesising fragments evidence and interpreting adoption patterns through the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) and Theory of Planned Behavior (TPB). There is a need for context-sensitive implementation strategies, infrastructural investment and improved teacher development to support AR/VR integration.
Detection of SQL Injection, XSS, and Command Injection Attacks in Web Payloads Using SVM, Random Forest, and XGBoost
Web application attacks, including SQL Injection (SQLi), Cross-Site Scripting (XSS), and Command Injection (CmdI), remain major threats to digital services. This study develops and evaluates an adversarial-aware protocol for multi-class malicious payload detection, focusing on accuracy, robustness against non-adaptive mutations, and practical inference feasibility. The protocol compares LinearSVC, Random Forest, and XGBoost with character-level neural baselines, namely character CNN and BiLSTM, and a transparent rule-based comparator. Evaluation integrates stratified sampling, deduplicated validation, mutation testing, SHAP-based interpretation, and end-to-end throughput measurement. Experiments used 49,998 stratified records from the SQLi-XSS-CommandInjection dataset in Google Colaboratory. On the internal test set, XGBoost obtained the best performance, achieving 99.28% accuracy and 99.32% macro F1-score. After removing 878 exact duplicate records for stricter re-evaluation, XGBoost maintained 99.21% accuracy and 99.24% macro F1-score, indicating that the findings were not driven solely by duplicate leakage. The complete preprocessing, feature extraction, and prediction pipeline reached an average CPU inference time of 0.832 ms per sample. SHAP analysis of Random Forest highlighted injection operators, script fragments, keyword hits, and structural tokens as discriminative features. The results provide a controlled benchmark, although validation on real HTTP logs remains future work.
AI-Assisted Development Tools and Team Dynamics in South African Software Engineering Teams
AI-assisted development tools are widely adopted in software engineering (SE), yet their effects on team dynamics and software delivery outcomes remain poorly understood in sub-Saharan African settings. This paper investigates how AI tool integration influences team roles, collaboration, skill requirements, and software delivery outcomes among South African software development professionals. A mixed-methods design was used, combining a structured survey with thematic analysis of open-ended responses from 40 participants across developer, tester, DevOps, and team lead roles. Multiple linear regression and Spearman's rank correlation were applied to quantitative data; thematic analysis followed the six-phase approach of Braun and Clarke. Findings show that GitHub Copilot was used by 75% of respondents. Interpersonal trust was the strongest predictor of development speed (β = 0.485, p = 0.017), exceeding all AI-specific variables in the model. AI use at the adoption onset reduced development speed; frequency of use increased it. Role transformation was reported by 95% of respondents and predicted team productivity. However, causal inference is not warranted given the cross-sectional design and reliance on self-reported measures. The findings are further constrained by a purposive sample of 40 drawn from networked professional communities, which limits statistical power and generalisability. To the authors’ knowledge, no prior published study has examined AI adoption and team dynamics within a South African SE population, using this combination of methods, though a systematic literature review of that literature was beyond the scope of this study.
Context-Aware Disaster Cause Mining from Indonesian Online News Using GA-Optimized Apriori: A Forest and Land Fire Case Study
Online disaster news contains reported cause information, but the narratives are unstructured and difficult to use for systematic disaster risk analysis. This study develops a context-aware disaster cause mining framework to extract and analyze reported cause-context association patterns from Indonesian online news. The framework integrates text-based cause extraction, contextual enrichment using population density and meteorological variables, GA-optimized Apriori-based Association Rule Mining, and merged rule interpretation. Disaster news records were transformed into transactions containing disaster type, reported causes, population density category, three-day rainfall category, and maximum temperature category. From 742 final transaction records, the Apriori process generated 200 initial association rules. After filtering rules with reported causes and contextual attributes in the antecedent and disaster type in the consequent, 97 target rules were retained. The empirical analysis focused on forest and land fire as a case study, producing 24 rules and 5 merged rule patterns. The strongest merged pattern was related to land burning, with a merged support of 0.1173. The findings show that the framework can organize disaster narratives into interpretable reported cause-context association patterns for disaster risk analytics. However, the results should not be interpreted as verified causal evidence.
Class-Level Behavior Analysis under Metric Disagreement in Imbalanced Multi-Label Indonesian Emotion Classification
This study aims to analyze class-level model behavior under metric disagreement in imbalanced multi-label Indonesian emotion classification, using the divergence between Macro F1 and Micro F1 as a diagnostic signal rather than a mere performance indicator. A machine-translated Indonesian version of the GoEmotions dataset, comprising approximately 58,000 samples across 28 fine-grained emotion categories, is used as the experimental setting. The translated dataset was not manually revalidated, and findings are scoped to this translated GoEmotions setting. Two transformer-based models are evaluated: IndoBERT, a monolingual Indonesian model, and DistilBERT, a multilingual model, both fine-tuned with class-specific threshold optimization. The results reveal opposing divergence patterns: IndoBERT achieves higher Micro F1 than Macro F1, indicating performance concentrated on high-frequency classes, while DistilBERT exhibits the reverse pattern, suggesting broader but less precise label activation. Per-class analysis further shows that most minority classes consistently fall into unstable or non-functional performance regimes across both models. This study concludes that aggregate metrics alone are insufficient for evaluating model behavior in imbalanced multi-label settings. A behavior-oriented interpretation framework for Macro–Micro F1 divergence and a regime-based class reliability categorization are proposed to support more structured and informative evaluation practices.
A Mixed Adversarial Awareness Technique for Improving Neural Network Defense
Neural Network (NN) models, particularly Convolutional Neural Networks (CNNs), have achieved remarkable performance in computer vision tasks but remain highly vulnerable to adversarial attacks. Existing defense techniques mainly focus on detecting adversarial examples and often show limited effectiveness when adversarial perturbations coexist with significant noisy inputs. To address this limitation, this study proposes a Mixed Adversarial Awareness Technique (MAAT) based on kernel density estimation and a Bayesian uncertainty estimator. Kernel density estimation is used to model data manifolds in the input subspace, while the Bayesian uncertainty estimator, inspired by the Dirichlet process, quantifies predictive uncertainty in the input space. The proposed technique was evaluated on three benchmark datasets, CIFAR-10, CIFAR-100, and SVHN, using four adversarial attack schemes, namely FGSM, BIM, JSMA, and C&W, as well as Gaussian noise injection. The LeNet ConvNet model was employed as the test classifier. Experimental results show that MAAT effectively flags adversarial and noisy instances, improving detection performance with AUC values ranging from 0.84 to 0.96, compared with 0.61 to 0.94 achieved by selected state-of-the-art techniques. These findings demonstrate that combining density-based manifold modeling with uncertainty estimation provides a robust defense against mixed adversarial and noisy inputs.
Transfer Performance and LIME Explanation of Ensemble Classifiers in Cross-Project Defect Prediction
Ensemble methods are widely used in cross-project defect prediction (CPDP), particularly in projects that lack sufficient historical data by training on external source projects. However, no prior study has compared Bagging, Boosting, and Stacking directly under a Leave-One-Project-Out (LOPO) protocol or examined whether within-project performance rankings carry over to the cross-project setting. We evaluated three ensemble classifiers on five NASA MDP datasets sharing a common Halstead and McCabe feature schema. SMOTE is applied exclusively to pooled source data to prevent leakage into the target. A no-SMOTE baseline isolates the contribution of source-only SMOTE. LIME explanations are aggregated over thirty instances per model to assess feature importance consistency across the project boundary. Within-project evaluation shows Stacking achieves the highest F1 on four of five datasets, peaking at 0.503 on KC1. Under LOPO, these rankings reverse as Bagging and Boosting transfer more reliably, while Stacking's F1 drops by up to 0.258 points. Source-only SMOTE consistently improves transfer across all targets and ensembles. LIME consistency analysis produces undefined Spearman rank correlations, indicating that thirty-instance aggregation is insufficient to produce stable rank vectors for 21-feature datasets. To the best of our knowledge, this is the first study to compare all three ensemble strategies under LOPO on a shared NASA dataset feature schema. Particularly with a no-SMOTE control, aggregated LIME analysis, and a pilot meta-feature study identifying dataset size as the most actionable label-free predictor of ensemble suitability for CPDP deployment.
Handcrafted Feature Ablation for Batik Nitik Classification Under Provenance-Aware Evaluation
This study re-examines Batik Nitik classification using a leakage-safe provenance-aware evaluation protocol to determine which handcrafted descriptors make a substantive contribution to performance and whether saturated results persist after provenance-based partitioning. Batik Nitik 960 was represented using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Gray-Level Co-occurrence Matrix (GLCM) descriptors, and grayscale intensity moments. Descriptor ablation, classifier benchmarking, cosine-similarity baselines, four-setting leave-one-provenance-group-out sensitivity analysis, and a supplementary image-level split comparison were evaluated using in-pipeline preprocessing. All HOG-containing feature sets achieved 0.9833 cross-validation accuracy and 1.0000 hold-out accuracy. On fused features, SVM, KNN (Euclidean), and KNN (cosine) achieved 1.0000 hold-out accuracy, while Random Forest reached 0.9958. Raw-pixel, HOG-only, and fused-feature cosine baselines also reached 1.0000 hold-out accuracy. A supplementary image-level HOG-SVM split also produced 1.0000 accuracy. This study contributes a provenance-aware benchmark diagnosis for Batik Nitik classification by identifying HOG as the strongest standalone handcrafted descriptor and by cautioning against deployment-ready interpretation of saturated closed-set accuracy.
Predicting Student Performance to Support Adaptive Content Delivery: A Random Forest Approach
This study addresses the prediction-to-action gap in student performance analytics by proposing an interpretable framework that transforms predictive risk scores into adaptive content recommendations. Rather than only identifying at-risk students, the framework integrates performance prediction, interpretable rule extraction, and decision-support simulation to guide adaptive learning interventions. The study used the Open University Learning Analytics Dataset (OULAD), comprising 6,937 student records after filtering and preprocessing from the original 32,593 records. A Random Forest-based framework was adopted because of its interpretability and rule-extraction capability, although XGBoost achieved slightly higher predictive performance. The framework consists of three components: student performance prediction, interpretable decision rule extraction, and a decision-engine simulation for adaptive content recommendation. The predictive model achieved 87.22% accuracy and an AUC-ROC of 0.932. Rule extraction generated 20 human-readable rules with an average of 2.0 conditions per rule, an interpretability score of 1.000, and 81.6% fidelity to the full Random Forest model. The decision-engine simulation classified students by risk level and produced corresponding adaptive recommendations. An estimated Adaptation Gain metric indicated a potential 53.54% improvement in projected student success rates under conservative simulation assumptions. The proposed framework connects prediction with actionable recommendations to support educational decision-making, although real-world intervention validation remains necessary.
A Robustness-Oriented Evaluation of LSTM, GRU, and Hybrid LSTM-GRU Models for ANTM.JK Stock Price Forecasting
Accurately forecasting stock prices remains challenging because of the nonlinear and volatile nature of financial markets, particularly during periods of heightened uncertainty, such as the COVID-19 pandemic. This study evaluates the robustness of three models, LSTM, GRU, and Hybrid LSTM-GRU, for ANTM.JK stock price forecasting using a volatility-oriented evaluation framework. Historical stock data from September 2005 to May 2022 were transformed into supervised time-series datasets using a 15-lag sliding window. The model performance was evaluated using baseline prediction accuracy, 5-fold chronological cross-validation consistency, and synthetic stress scenarios consisting of controlled price drops, price rises, and high-volatility noise. Evaluation metrics included RMSE, MSE, MAE, R, and R^2. The GRU model delivered the top baseline prediction results, achieving the smallest RMSE of 52.95 and MAE of 28.14. In cross-validation, the LSTM model recorded the lowest average RMSE of 119.41. Meanwhile, the Hybrid LSTM-GRU exhibited the highest prediction consistency and robustness across various synthetic stress scenarios. In contrast to earlier research that mainly focused on prediction precision, this study presents a comprehensive framework for evaluating robustness. This framework combines baseline accuracy, consistency through cross-validation, and an analysis of synthetic stress scenarios. The generated robustness map offers a systematic interpretation of model strengths across diverse evaluation goals, facilitating a more thorough assessment of stock-forecasting models in different market environments.
UTAUT-Based Analysis of Factors Associated with Microsoft Teams Use in Digital Learning among Undergraduate Students
This study examined factors influencing Microsoft Teams adoption in digital learning using the Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative cross-sectional survey was conducted with 268 undergraduate students, and data were analyzed using reliability testing, Confirmatory Factor Analysis (CFA), classical assumption testing, and multiple linear regression. The results showed that Performance Expectancy, Effort Expectancy, and Social Influence significantly influenced Behavioral Intention, while Facilitating Conditions and Behavioral Intention significantly influenced Use Behavior. The model explained 60.2% of the variance in Behavioral Intention and 46.4% of the variance in Use Behavior. CFA demonstrated acceptable fit for most indices, although RMSEA indicated marginal fit. Performance Expectancy was the strongest predictor of Behavioral Intention, whereas Behavioral Intention was the strongest predictor of Use Behavior. Unlike previous studies focusing primarily on Behavioral Intention, this study also examined its relationship with self-reported Use Behavior among Indonesian undergraduates. The findings highlight the importance of usefulness, ease of use, social support, and facilitating conditions in promoting Microsoft Teams adoption for digital learning.
A Hybrid LSTM-GNN-Q-Learning Model for Zero-Day Attack Detection: Evaluation on CICIDS2017 with Simulated Zero-Day Setting
Zero-day attacks exploit previously unseen vulnerabilities, making them difficult to identify using signature-based approaches. Their ability to bypass conventional detection mechanisms can result in significant financial losses, system compromise, and data breaches. To address this challenge, this study proposes a Hybrid Predictive Deep Learning (HPDL) model that integrates the Long Short-Term Memory (LSTM) network for modelling temporal relationships, Graph Neural Networks (GNN) for structural relationship modelling, and Q-Learning for feature weighting and adaptive decision making. The model was evaluated on CICIDS2017 dataset under a simulated zero-day setting by holding out four attack types (Brute Force, SQL Injection, XSS, and Infiltration), totaling 2,179 zero-day samples deliberately excluded from training and validation and used only for final testing. Experimental results show that the proposed HPDL model achieved a zero-day attack detection accuracy of 99.63% and F1-score of 0.9970, outperforming LSTM-only and GNN-only baseline models, which achieved accuracies of 98.5% and 85.0%, respectively. These results indicate that integrating temporal, structural, and reinforcement learning paradigms provides an effective approach for zero-day attack detection.
Comparative Evaluation of BiLSTM-CNN, XGBoost, and Ridge Regression for Heart Disease Classification on the Cleveland Dataset
Transformers have become the dominant architecture for tabular data modelling in natural language processing; however, their effectiveness for numerical tabular classification on modest sized and moderately imbalanced datasets remains unclear. This study evaluates the performance of hybrid deep learning and classical machine learning models which use the Cleveland Heart Disease dataset with 297 complete observations and was artificially constructed from 13 clinical features. The models examined include BiLSTM-CNN, Random Forest, XGBoost, Logistic Regression, and Ridge Regression. An experimental comparative approach was adopted under identical preprocessing, training conditions, and evaluation metrics, including accuracy, recall, F1-score, and Area Under the Curve (AUC). Results show that BiLSTM-CNN achieved the highest recall (0.8478), demonstrating strong minority class detection capability. Random Forest and XGBoost produced the best-balanced performance with 81.67% accuracy and the BiLSTM-CNN has the best F1-score of 0.8364, while Ridge Regression achieved the highest AUC (0.8945). This study provides empirical evidence that hybrid recurrent and ensemble models perform optimally on a small to medium sized Cleveland Heart Desease numerical tabular datasets without pre-training, offering practical guidance for Cleveland Heart Disease tabular clinical classification tasks, and no external validation was performed.
TELOS Feasibility Evaluation of the e-HakCipta System: Evidence from Intellectual Property Centers in Samarinda
This study evaluates the feasibility of the updated e-HakCipta system developed by the Directorate General of Intellectual Property (DJKI), Ministry of Law and Human Rights, based on user perceptions at Intellectual Property Centers (Sentra KI) in Samarinda. A descriptive quantitative approach was employed using a structured Likert-scale questionnaire validated by three domain experts. Data were collected from seven participating institutions, each represented by one designated e-HakCipta user. The evaluation applied the TELOS framework, covering Technological, Economic, Legal, Operational, and Schedule feasibility. The results show that all dimensions exceeded the feasibility threshold. Economic feasibility achieved the highest mean score (4.33), followed by Schedule (3.90), Technological (3.86), Operational (3.76), and Legal feasibility (3.67). The overall TELOS composite score of 3.90 indicates that the system is feasible. These findings suggest that the updated e-HakCipta system improves cost efficiency, registration speed, and interface usability, although legal compliance verification, plagiarism detection, and system stability require further enhancement.
Evaluating Rural E-Government Web Service Using Design Thinking and COBIT 2019: A Case Study of Indramayu Regency
This study evaluates the maturity of public web services in Indramayu Regency, Indonesia, using a Design Thinking methodology combined with the COBIT 2019 Process Performance Model, aiming to clarify the digital governance gap in rural public service delivery. The research applies the five-stage Design Thinking process (Empathize, Define, Ideate, Prototype, Test) as an evaluation lens. Data were collected from 45 respondents through interviews, questionnaires, direct observations, and workshops with village officials. The COBIT 2019 Process Attribute Achievement Model was used to quantify capability maturity levels across each Design Thinking phase for 14 public web services in the regency. Most public web services scored only ‘Manage’ in the Empathize phase, with BPS Indramayu and PPID reaching ‘Established’. The Ideate, Prototype, and Test phases were predominantly ‘Performed’, with only BPS Indramayu and SIPADU reaching 'Managed'. These findings indicate that most platforms function primarily as informational websites with limited user-driven innovation, prototyping, and structured testing. This study offers a novel integrative framework that combines human-centered Design Thinking with COBIT 2019 governance assessment to assess the public sector web services of digital transformation in decentralized rural governance contexts, providing a scalable, empirically grounded model applicable to other rural communities.
Enhancing Javanese Emotion Classification: A Comparative Study of Cross-Lingual, Supervised, and Hybrid Transfer Learning using IndoBERTweet
This research investigates transfer learning efficacy for five-class emotion classification in Javanese Ngoko. A parallel Indonesian–Javanese Ngoko corpus was synthesized by translating 5,400 samples from the PRDECT-ID dataset using machine translation, with translation quality verified via a preliminary expert validation sample. Using IndoBERTweet as the backbone architecture, three paradigms were evaluated: zero-shot transfer (E1), fully supervised learning (E2), and cross-lingual transfer learning (E3) with identical hyperparameters. Empirical results indicate that the cross-lingual transfer (E3) setup achieved peak performance (67,5% accuracy; 0,67 weighted F1) under the evaluated dataset and experimental setting. Per-class analysis identified that positive affect (Happy) showed cross-lingual stability, whereas negative emotions (Sadness, Fear) suffered degradation due to lexical divergence between the two languages. Training dynamics revealed early-onset overfitting, suggesting model capacity exceeds current dataset density. This work establishes a baseline benchmark for Javanese emotion classification and provides a reproducible machine-translated parallel corpus, emphasizing the need for future validation with native-speaker data to mitigate translation bias.
Residual-Based Hybrid SARIMA–LSTM for Bali Tourism Demand Forecasting Using Google Trends
Accurate tourism demand forecasting is essential for destinations characterized by strong seasonality, nonlinear fluctuations, and post-pandemic recovery uncertainty. This study develops a residual-based hybrid SARIMA–LSTM model for forecasting monthly international tourist arrivals to Bali, Indonesia, using historical arrival data and Google Trends search query data. The dataset covers January 2009 to December 2024, comprising 192 monthly observations. A chronological split was applied, with January 2009 to December 2022 used for training and January 2023 to December 2024 used for testing. SARIMA was employed to capture linear and seasonal structures, while LSTM was used to learn nonlinear residual patterns. The proposed model was compared with SARIMA, Random Forest, standalone LSTM, and SARIMA–RF using RMSE, MAPE, and R². The SARIMA–LSTM model achieved the best performance, with RMSE = 35,915.36, MAPE = 5.64%, and R² = 0.68, compared with SARIMA, which obtained RMSE = 37,052.68, MAPE = 5.70%, and R² = 0.65. These findings indicate that residual-based hybridisation provides incremental forecasting improvement. However, the independent contribution of Google Trends is not separately isolated in this study and should therefore be interpreted cautiously as a complementary behavioural signal within the proposed forecasting framework.
Spatio-Temporal Graph-Based Hotspot Analysis of Earthquake Events Using Spatial Autocorrelation and Community Detection in Indonesia
Analysis of clustered seismic regions is important for understanding seismic activity patterns in tectonic regions such as Indonesia. However, conventional spatial statistical approaches generally analyze earthquake events independently and fail to capture complex spatio-temporal relationships. This study proposes a graph-based spatio-temporal hotspot analysis approach integrating spatial autocorrelation and community detection to identify regional seismic interaction patterns. The dataset used consists of 3,000 earthquake events from 2008–2025. Spatial autocorrelation was analyzed using Moran’s I, while earthquake relationships were modeled using a spatio-temporal graph with spatial and temporal thresholds of ≤400 km and ≤60 days. The results showed significant positive spatial autocorrelation with Moran’s I = 0.3367 (p = 0.001). The resulting graph consisted of 3,000 nodes and 22,896 edges, revealing substantial regional-scale connectivity and 14 major clusters with a modularity score of 0.7405, indicating a strong community structure. Degree centrality analysis identified highly connected nodes with a maximum degree of 77. These findings indicate that integrating spatial autocorrelation and graph analysis provides a more comprehensive representation of seismic interaction patterns and may support future seismic risk assessment in tectonically active regions.
Explainable AI for Water Quality Classification Using Ensemble Stacking
This study proposes a robust and interpretable machine learning framework for water quality classification using a publicly available water quality dataset containing 7,996 samples and 20 physicochemical features with an imbalanced class distribution (88.59% majority and 11.41% minority). The study addresses the critical issue of biased classification toward the majority class, which can lead to risk-prone misclassification of unsafe water. An ensemble stacking model combining XGBoost, LightGBM, and CatBoost with a Random Forest meta-learner (passthrough) was developed using an anti-leakage pipeline integrating RobustScaler and SMOTE within stratified 80:20 train–test cross-validation, while hyperparameter tuning was optimized using F1-score to improve minority-class performance; SHAP was further applied for global and local explainability. The proposed model achieved an F1-score of 0.8563 for the minority class and a ROC-AUC of 0.9846, indicating strong discriminative performance, while SHAP analysis identified ammonia as the most influential feature and revealed that False Negative errors were mainly caused by complex feature interactions. The study contributes an integrated framework combining stacking ensemble learning, anti-leakage evaluation, and SHAP-based global–local interpretation to support more reliable and transparent water quality classification; however, the findings are currently limited to a single dataset and and require multi-dataset validation.
PADLOCK: A Context-Aware On-Device System for Android Permission-Risk Assessment – A Tanzanian Case Study
Android applications often request sensitive permissions beyond their functional needs, creating privacy and security risks, especially in financial and digital lending apps. Existing Android permission mechanisms provide limited visibility into how permissions are used at runtime. This study proposes PADLOCK, a context-aware prototype for event-driven permission monitoring and contextual risk assessment. The methodology combined a survey-based user study (n = 600), analysis of 3,816 applications, machine learning development, and Android prototype implementation. A compact deep neural network, ShieldAI, was trained on permission-based features with heuristic risk labels to classify applications as benign or potentially higher-risk. The model was integrated into PADLOCK to support on-device monitoring and user-guided permission decisions. On a held-out test set, ShieldAI achieved 96.73% accuracy, 94.82% precision, and 92.42% recall. Controlled on-device testing showed an inference latency of approximately 2–3 ms per prediction and indicated reduced higher-risk permission-granting behaviour in PADLOCK-assisted evaluations. This study contributes an integrated Android prototype combining contextual permission analysis, machine-learning-based risk assessment, and event-driven monitoring. However, the findings remain limited to the evaluated dataset and controlled conditions, and broader real-world validation is required.