Journal of Information Systems and Informatics
Journal of Information System and Informatics
Asosiasi Doktor Sistem Informasi Indonesia
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Antecedents of Citizen-Centric E-Government Adoption: A Systematic Literature Review and Future Research Agenda
Although e-government adoption has been widely studied, limited attention has been given to citizen-centric adoption, despite citizens’ willingness being central to e-government success. This study systematically synthesises the literature to identify and conceptualise the multidimensional antecedents of citizen-centric e-government adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, peer-reviewed studies published between 2004 and 2025 were retrieved from Scopus and Web of Science. A total of 87 studies were analysed to address four research questions. The review identifies 21 antecedents grouped into six dimensions: trust factors, technology acceptance constructs, system quality, user competency, enabling conditions, and security and privacy concerns. The Unified Theory of Acceptance and Use of Technology (UTAUT) emerged as the most frequently applied framework, followed by the Technology Acceptance Model (TAM). The findings show that citizen adoption is multidimensional and is best explained through integrated theoretical perspectives. The proposed taxonomy offers researchers a structured basis for empirical validation and provides policymakers with theory-informed guidance for improving adoption. Without addressing these interconnected antecedents, e-government may continue to operate alongside, rather than transform, traditional service delivery.
Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
AI-Enabled E-Learning in Correctional Centres: A Systematic Literature Review
Artificial Intelligence (AI) is increasingly transforming education through personalization, automation, and data-informed decision-making, yet its application in correctional education remains underexplored. This study examines the implementation of AI-enabled e-learning in correctional centres worldwide, emphasizing secure, adaptive, and ethical deployment. A systematic literature review following the PRISMA framework analysed peer-reviewed studies published between 2019 and 2025. No empirical studies were identified that evaluated fully AI-enabled learning systems on-site in correctional settings. Instead, the available evidence focuses on AI-adjacent educational technologies, secure offline platforms, and adaptive learning management systems. Findings indicate that digital education is increasingly feasible in correctional environments, although implementation is constrained by security protocols, limited infrastructure, and resource availability. Reported benefits include improved learner motivation, autonomy, and digital literacy, while evidence linking higher education to reduced recidivism remains cautious. AI-driven adaptive and personalized learning therefore remains largely aspirational. Significant ethical and legal gaps also persist, particularly regarding algorithmic bias, privacy, and data governance. Correctional education is consequently at a transitional stage requiring stronger regulation, participatory design, and longitudinal research to ensure that AI supports rehabilitation without reinforcing existing inequalities.
Association Rule Mining for Uncovering Co-occurring Skill Sets in Multi-Portal ICT Job Advertisements
Rapid changes in the ICT labour market require stakeholders to understand not only which skills are demanded but also how these skills occur together. However, existing studies provide limited insights into co-occurring skill demands in ICT jobs. This study addresses this gap by uncovering skill sets that frequently appear together in ICT job descriptions. A total of 15,653 unique ICT job advertisements published between 2020 and 2025 were collected from LinkedIn, AjiraYako and Mabumbe to capture regional and international contexts. Guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM), association rule mining using the Apriori algorithm identified frequent itemsets and generated rules evaluated using support, confidence and lift. The analysis revealed strong co-occurrences among programming skills, such as C# and C++ (confidence = 1.00; lift = 2.41), although this combination occurred in a small proportion of advertisements (support = 0.0096). Java and Python showed the highest support (0.1818), with confidence values of 0.51 and 0.42 and a lift of 1.19, which indicate a positive association despite belonging to different application domains. By uncovering skill associations beyond individual skill frequencies, the study provides richer labour market intelligence for workforce development and policy formulation.
Machine Learning Models for Early Gingival Health Screening Using Routine Clinical and Behavioral Data
This study addresses the high prevalence of periodontal disease in Indonesia by developing an accessible, early screening tool that overcomes the limitations of existing AI approaches, which heavily rely on costly radiographic imaging. We developed a machine learning framework utilizing demographic, clinical, behavioral, and medical-history data from 500 patients at the Maninjau Community Health Centre. Four algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost—were evaluated using stratified 5-fold cross-validation and ROC curve assessment. The results demonstrate that Random Forest achieved the highest overall classification performance, attaining an accuracy of 95.2%, an AUC of 0.983, and an F1-score of 0.9521, with superior capability across all categories (Normal, Gingivitis, and Periodontitis). To demonstrate practical applicability, this model was deployed as a Streamlit-based prototype decision-support application for real-time clinical use. This research provides significant value by demonstrating that routinely collected, non-invasive clinical and behavioral data can serve as potent predictors for periodontal screening, eliminating the dependency on expensive imaging. This offers a cost-effective digital health framework suitable for primary healthcare settings. Future research will focus on validating this model using larger, multi-center datasets to ensure broader generalizability.
Comparative Sentiment Analysis of Profits Anywhere Application Reviews Using KNN and Naïve Bayes
User reviews on Google Play Store provide valuable information regarding application service quality and user satisfaction. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms for sentiment analysis of reviews of the Profits Anywhere application, a digital business platform offering affiliate marketing and financial services. A total of 1,037 reviews were collected through web scraping and subsequently underwent data cleaning and validation, resulting in 957 valid reviews used in the analysis, consisting of 674 positive and 283 negative reviews. The dataset was subjected to a comprehensive text preprocessing pipeline, including text cleaning, case folding, tokenization, stopword removal, and stemming, followed by feature representation using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. The data were partitioned using an 80:20 stratified train–test split, while hyperparameter optimization was conducted using GridSearchCV with 5-fold stratified cross-validation. Experimental results demonstrate that KNN outperformed Naïve Bayes on the evaluated dataset and experimental configuration, achieving an accuracy of 92.19%, weighted precision of 92.23%, weighted recall of 92.19%, and weighted F1-score of 92.21%. In contrast, Naïve Bayes achieved an accuracy of 88.54%, weighted precision of 89.04%, weighted recall of 88.54%, and weighted F1-score of 88.70%. These findings provide empirical evidence regarding algorithm selection for sentiment analysis of application reviews in the fintech and digital business domains.
Securing Educational Financial Data Against Insider Threats: A Hybrid Blockchain Approach with Merkle Tree Aggregation
Financial information systems in educational institutions face insider threats where privileged administrators can manipulate database records undetected by conventional security. This study proposes a Hybrid Blockchain architecture integrating Merkle Tree Aggregation and a Reversal Entry mechanism to establish a tamper-evident financial audit trail and address these data integrity gaps. Developed via the Design Science Research Methodology (DSRM), the system implements three cryptographic layers: a SHA-256 Recursive Transaction Hash Chain for local integrity, a Keccak-256 Merkle Tree for daily aggregation, and public Ethereum anchoring. Empirical evaluations demonstrate successful cryptographic integrity verification across all five database manipulation attack scenarios with zero false positives only under the five tested normal operational scenarios, relying strictly on deterministic hashing rather than AI-based anomaly detection. Computational latency remains consistent at 3.45 ms per transaction. Based on Sepolia testnet data under mainnet-equivalent projections, the 1,000:1 aggregation compression yields 99.90% cost efficiency compared to pure public blockchains, with sensitivity analysis confirming financial viability across volatile gas prices and exchange rates. These findings indicate the technical and economic feasibility of adopting a Hybrid Blockchain for detecting tampering and preserving data integrity in educational institutional financial data under the evaluated scenario.
Experimental Evaluation of Avalanche Effect and Hash Consistency in SHA-256, SHA3-256, and BLAKE2b-256 for Smart Grid Data Integrity Verification
Ensuring the integrity of Smart Grid data is essential for preventing unauthorized data modification during transmission and storage. Cryptographic hash functions provide an efficient mechanism for integrity verification; however, their diffusion characteristics and computational performance under identical conditions require further evaluation. This study experimentally compares SHA-256, SHA3-256, and BLAKE2b-256 for Smart Grid data integrity verification using avalanche effect analysis, hash consistency evaluation, and computational performance benchmarking with a standardized 256-bit digest length. The results show that all evaluated algorithms achieved avalanche effect values close to the theoretical 50% criterion (SHA-256: 49.97%, SHA3-256: 49.96%, and BLAKE2b-256: 50.04%) and maintained a 100% hash consistency rate, indicating comparable diffusion capability and deterministic behavior. In terms of computational performance, BLAKE2b-256 achieved the shortest average execution time (11.14 ms), outperforming SHA-256 (13.24 ms) and SHA3-256 (16.87 ms). These findings indicate that all evaluated algorithms are suitable for Smart Grid data integrity verification, while BLAKE2b-256 provides the highest computational efficiency under the experimental conditions. The results are limited to the experimental evaluation of cryptographic hash function behavior and do not represent a comprehensive assessment of Smart Grid cybersecurity.
A Temporal Patient Risk Timeline Framework for Longitudinal Clinical Deterioration Surveillance Using Hospital Data Warehouse and Machine Learning
Clinical deterioration among hospitalized patients remains a major challenge in inpatient care management. Conventional deterioration monitoring approaches frequently rely on isolated physiological observations and static threshold-based scoring systems, which may inadequately represent the longitudinal nature of patient instability. This study aimed to develop and evaluate a retrospective temporal patient risk timeline framework for clinical deterioration surveillance using hospital data warehouse and machine learning. A retrospective longitudinal deterioration surveillance framework was developed using hourly inpatient observations derived from hospital operational and clinical data sources. A temporal XGBoost model was developed to predict ICU transfer within the subsequent 24-hour observation window under severe class imbalance conditions. To evaluate the additional value of temporal feature engineering, baseline non-temporal models including logistic regression, random forest, and non-temporal XGBoost were also developed using static observation-level variables. Operational feasibility was retrospectively demonstrated through a dashboard prototype integrating deterioration prioritization, patient timeline visualization, and longitudinal surveillance. The final dataset consisted of 20,181 inpatient admission episodes represented by 2,233,143 hourly observations. ICU transfer occurred in 712 admissions. Because the prediction task was formulated as hourly deterioration surveillance, ICU transfer labels represented only 0.65% of all hourly observations, indicating severe class imbalance. The temporal XGBoost model achieved an AUROC of 0.606 and an AUPRC of 0.009. While predictive discrimination was modest, the framework enabled continuous longitudinal representation of patient deterioration and retrospective surveillance of clinical instability. The findings suggest that temporal patient representation may provide operational and interpretative value beyond static snapshot-based deterioration monitoring approaches.
Green Software Engineering for Sustainable IT Services: A Scopus-Based Systematic Literature Review and Bibliometric Mapping
This study maps the development of Green Software Engineering (GSE) research and examines its contribution to sustainable IT services. A Scopus-based Systematic Literature Review (SLR) was combined with bibliometric mapping. Records were retrieved on May 6, 2026 using TITLE-ABS-KEY("Green Software"), screened according to PRISMA, and analyzed with VOSviewer. After applying document-type and language criteria, 108 English-language Scopus-indexed journal articles were included. The findings indicate that GSE is an emerging but steadily growing field, with publication activity increasing markedly after 2016. Spain was the leading contributor, while major research themes included energy efficiency, energy consumption, sustainable development, software design, Green Computing, software sustainability, and Green IT. This study contributes by integrating systematic review evidence with bibliometric mapping to provide a consolidated and traceable overview of GSE in the context of sustainable IT services. Practically, the findings highlight research-informed priorities for IT service planning, including energy efficiency, hardware efficiency, carbon awareness, sustainability measurement, and climate commitments. The results are limited to Scopus-indexed evidence retrieved using the keyword “Green Software” and should not be interpreted as representing all global GSE research.
An Explainable PCA-XGBoost Model for Predicting Bloodstream Infection in Hemodialysis Patients
Bloodstream infection (BSI) is a life-threatening complication in hemodialysis (HD) patients with catheter-based vascular access, carrying mortality rates of 15–50%, yet early detection remains challenging due to high-dimensional clinical data with significant multicollinearity. This study develops a BSI prediction model integrating Principal Component Analysis (PCA), XGBoost, Synthetic Minority Oversampling Technique (SMOTE), and dual Explainable AI (XAI) methods to improve predictive performance and clinical transparency. A dataset of 391 HD patients (18.9% BSI-positive) was preprocessed with encoding, standardization, and median imputation. PCA reduced 37 features to 29 components retaining 95.0% variance; SMOTE was applied inside each cross-validation fold to prevent leakage; and hyperparameters were optimized via RandomizedSearchCV. The proposed model achieved 83.5% accuracy, 33.3% recall, 43.5% F1-score, 85.5% AUC-ROC, and 0.643 PR-AUC, outperforming the baseline (81.0% accuracy, 0.0% recall, 0.190 PR-AUC). Bootstrap 95% confidence intervals and Brier score calibration are reported; results require cautious interpretation given the small positive test set (n=15). SHAP and LIME identified PC1 (hematological parameters) and PC2 (inflammatory markers) as dominant predictors. This study explores PCA, XGBoost, and dual XAI integration for BSI prediction in HD patients, an approach not extensively examined in this context. External multicenter prospective validation is required before clinical deployment.
A Socio-Technical Assessment of Information Security Management Using KAMI Index, ISO/IEC 27001:2022, and User Awareness
ISO/IEC 27001:2022 is one of the certifications for Information Security Management Systems (ISMS). The study employs a mixed descriptive approach, analyzing maturity using the KAMI Index 5.0, ISO/IEC 27001:2022 clause implementation as a gap assessment, and user awareness of the ISMS, examined through socio-technical system theory at a university in Semarang. The results reveal a gap between the two subsystems: the technical subsystem shows high overall readiness but is not fully optimal, with weaker domain in Personal Data Protection (level II). The KAMI Index places the university's overall maturity at level V, while the ISO/IEC 27001:2022 gap assessment shows several clauses still unimplemented. Meanwhile, the social subsystem for user awareness revealed that the ISMS was less than optimal according to user interviews, even though the test achieved a score of 81.2% and was rated Very Worthy. Suitable standard operating procedures (SOPs) were also found lacking for several clauses and indicators. Socio-technical systems theory emphasizes joint consideration of social and technical elements in designing and implementing complex organizational systems such as information security; applying it here shows both subsystems must be evaluated holistically rather than separately.
Comparative Analysis of Four Machine Learning Classifiers for Indonesian Hoax News Detection
Across Indonesian online platforms, fabricated news spreads faster than fact-checkers can confirm. Because much of the literature relies on resource-intensive deep models, one applied question stays unsettled: which lighter, more transparent classifier best detects Indonesian hoaxes? We assessed four algorithms, Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Extreme Gradient Boosting (XGBoost), on 1,116 Indonesian articles from the MAFINDO/TurnBackHoax repository, using one shared preprocessing pipeline and two feature schemes, Bag-of-Words (BoW) and TF-IDF, under an 80:20 stratified split. On the held-out test set, Random Forest with BoW performed best at 98.66% accuracy and 98.59% macro F1-score, missing three of 224 cases, with XGBoost next at 98.21%. Under repeated five-fold cross-validation, however, XGBoost with BoW attained a significantly higher mean (98.36% versus 97.44% macro F1), so the two ensembles are best regarded as closely competitive rather than decisively ranked. BoW also beat TF-IDF for every classifier, most sharply for Naïve Bayes (84.70% versus 70.19% F1). Because the study uses lexical features alone on a single dataset, the findings indicate that classical ensembles can rival reported deep-learning figures at far lower cost, rather than forming a universal conclusion; broader validation across other sources and periods is still needed.
User-Centered Design of Marker-Based Augmented Reality Learning Media to Improve Informatics Learning Effectiveness
Computer science education in schools still relies heavily on traditional teaching methods, resulting in low student motivation, particularly in topics related to computer hardware components. This study develops an interactive Augmented Reality (AR) learning medium with Marker-Based Tracking using a User-Centered Design (UCD) approach to improve Informatics Learning effectiveness among 73 junior high school students at SMP Negeri 28 OKU. The UCD method was implemented through four stages: establishing requirements, designing, prototyping, and evaluating. Learning effectiveness was measured using N-Gain, a paired sample t-test, and Cohen's d based on pre-test and post-test scores. User experience was evaluated using the User Experience Questionnaire (UEQ), covering six dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. The N-Gain score reached 0.937, classified as "High." The paired t-test yielded a statistically significant difference between pre-test and post-test scores (p < 0.001), and Cohen's d was 1.757, indicating a "Very Large" effect size. UEQ evaluation results across all six dimensions exceeded the benchmark threshold for the "Excellent" category. These findings indicate improved learning outcomes in the evaluated class and positive user experience with the developed AR medium.
User Acceptance of E-Kinerja for Employee Additional Income Policy: A TAM Study in Central Lombok
The increasing emphasis on professionalism, accountability, and performance management among State Civil Apparatus (ASN) has encouraged government institutions to adopt digital performance systems. This study examines user acceptance of the e-Kinerja application supporting the Employee Additional Income (TPP) policy at the Environmental Agency of Central Lombok Regency using the Technology Acceptance Model (TAM). A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0.9.9 was applied to questionnaire data from 41 ASN employees. The model included Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual Use (AU), with AU measured through self-reported indicators rather than system-generated logs. The measurement model demonstrated satisfactory reliability and convergent validity, although several HTMT values exceeded the recommended threshold. Structurally, PEOU significantly affected PU (β = 0.826; p < 0.001), PU affected ATU (β = 1.070; p < 0.001), ATU affected BI (β = 0.842; p < 0.001), and BI affected AU (β = 0.823; p < 0.001), whereas PEOU did not significantly affect ATU (β = −0.346; p = 0.212). The model explained 68.2% of PU, 65.3% of ATU, 70.9% of BI, and 67.7% of AU. The findings indicate that e-Kinerja acceptance is primarily driven by perceived usefulness, attitudes, and behavioral intention. Practical improvements should emphasize TPP transparency, real-time feedback, data accuracy, simplified reporting, user training, and technical assistance to support adoption.
Interpretable Feature-Scenario Analysis for Ethereum Transaction Anomaly Detection Using Random Forest and XGBoost
The substantial and imbalanced volume of Ethereum transactions presents significant challenges for anomaly detection, especially when labels serve as proxies for execution errors rather than confirmed fraud. An interpretable feature-scenario framework was developed utilizing Logistic Regression, Random Forest, and XGBoost on 4,604,555 unique transactions. The isError attribute was employed as a proxy anomaly label. Data splitting occurred prior to address encoding; encoders were trained exclusively on the training set, unseen wallets were assigned a reserved code, and Random Under Sampling (RUS) was applied solely to training data. Evaluation incorporated both an imbalanced random test set and future-block validation. Among 920,911 random-test transactions (3.59% anomalies), Random Forest, excluding the Hour feature and without resampling, achieved optimal operational performance: 0.8204 precision, 0.6716 recall, 0.7386 F1-score, 0.7820 PR-AUC, 0.7338 MCC, and a 0.0055 false-positive rate. Application of RUS increased recall to 0.9035 but reduced precision to 0.3088, resulting in 69.12% of 96,703 alerts being false positives. Future-block validation further reduced PR-AUC to 0.0177 and MCC to 0.0678, indicating a substantial distribution shift. SHAP identified destination-wallet encoding and BlockHeight as the most influential model features, while LIME provided local, non-causal explanations. The primary contribution is an interpretable feature-scenario and validation framework; however, verified malicious labels and dynamic graph representations are still required for operational deployment.
Formulating Application Portfolios from IS/IT Strategy and Business Processes: A Systematic Literature Review
This study systematically examines how application portfolios are formulated from information systems/information technology (IS/IT) strategy and business processes. Following the Kitchenham and Charters guidelines and PRISMA reporting logic, searches across seven sources yielded 844 records. After duplicate removal, screening, full-text assessment, and quality evaluation, 22 primary studies published between 2015 and 2025 were included. The synthesis identifies five categories of approaches: Enterprise Architecture, IS/IT Strategic Planning, Application Portfolio Management and Portfolio Assessment, Model/Framework Development and Design Science, and Resource Allocation and Portfolio Decision Analysis. Across these categories, application candidates are derived through strategic analysis, business-process analysis, enterprise architecture modeling, portfolio assessment, and decision-analysis techniques. However, existing approaches remain fragmented and rarely provide a traceable process linking strategic objectives and business processes to application candidates, prioritization decisions, and final portfolio representations. To address this gap, the study proposes a four-stage conceptual model comprising input identification, application candidate derivation, evaluation and prioritization, and final portfolio representation. The proposed taxonomy and conceptual model provide an integrated foundation for connecting organizational strategy, business processes, application identification, portfolio evaluation, and representation, while supporting future development and empirical validation of application portfolio formulation methods across organizations.
Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction
Employee turnover prediction remains challenging in Human Resource (HR) analytics because class imbalance can reduce the ability of machine learning models to identify employees at genuine risk of leaving. This study develops and evaluates a comprehensive machine learning framework that balances minority-class detection and false-positive control. A publicly available HR dataset containing demographic, organizational, performance, and training-related attributes was analyzed using seven algorithms: Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, XGBoost, LightGBM, and CatBoost. Cost-sensitive learning and three resampling methods, SMOTEENN, ADASYN, and Tomek Links, were compared through stratified 10-fold cross-validation. Performance was evaluated using ROC-AUC, PR-AUC, Balanced Accuracy, Matthews Correlation Coefficient, G-Mean, Sensitivity, and Specificity, followed by threshold adjustment and SHAP analysis. Original LightGBM achieved the highest discrimination performance (ROC-AUC = 0.5975 ± 0.0546; PR-AUC = 0.2020 ± 0.0426), while cost-sensitive LightGBM produced the most balanced results (Balanced Accuracy = 0.5221 ± 0.0303; MCC = 0.0499 ± 0.0685). SHAP identified Department Type, Current Employee Rating, Training Cost, and Age as key predictors. Overall, integrating cost-sensitive learning, threshold optimization, and explainability improved model interpretability and practical utility for evidence-based HR decision-making processes in employee retention management and planning.
Application-Driven Parallel Differential Evolution: A Systematic Mapping Review of Scalable Optimization Applications
This systematic mapping review examines application-driven parallel Differential Evolution (DE) as a scalable optimization approach for engineering, energy, computing, and intelligent systems. The review is based on a Scopus-only corpus searched on 24 May 2026 using TITLE-ABS-KEY queries related to DE, parallel computing, distributed computing, GPU acceleration, and scalable optimization. From 220 records, 25 unique studies published between 2021 and 2026 were included after screening by year, document type, language, relevance, and methodological alignment. Because the corpus contains heterogeneous benchmark studies, application studies, and hybrid intelligent-system frameworks, the evidence was synthesized thematically rather than through meta-analysis. The synthesis distinguishes explicit parallel-DE implementations from broader scalable DE applications in which scalability is achieved through decomposition, model reformulation, or integration with computationally expensive systems. The findings indicate that GPU acceleration, CUDA, MPI migration, cooperative coevolution, Spark/Hadoop distribution, and resource-aware dispatch frequently report reduced computational cost while preserving or improving solution quality under the evaluated conditions. However, cross-study comparison remains limited by heterogeneous benchmarks, incomplete hardware reporting, inconsistent scalability metrics, and uneven baseline selection. This review contributes a cross-domain taxonomy linking application constraints, DE mechanisms, computational architectures, evaluation metrics, and reported outcomes.
Extending the Technology Acceptance Model with Perceived Immersive Learning for VR Usage in Higher Education
This study examines the adoption of Virtual Reality (VR)-based learning in Indonesian higher education by extending the Technology Acceptance Model (TAM) through Perceived Immersive Learning (PIL). The study investigates how perceived usefulness, perceived ease of use, PIL, behavioral intention, and self-reported usage behavior are related. A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to data collected from 180 university students with experience using VR-based immersive learning technologies. The measurement and structural models were analyzed using SmartPLS 4. Results show that perceived ease of use significantly influences perceived usefulness and PIL, while perceived usefulness also has a significant positive effect on PIL. In addition, PIL significantly affects behavioral intention, which subsequently influences self-reported VR usage behavior. The model explains 58.6% of the variance in behavioral intention (R² = 0.586), indicating substantial explanatory power. These findings demonstrate that immersive learning experiences play a central role in translating technology perceptions into behavioral intention and subsequent usage behavior. The study contributes to TAM literature by empirically positioning PIL as a mediating construct and offers practical insights for universities seeking to design more engaging, usable, and meaningful VR-based learning environments that can strengthen students' acceptance and sustained engagement with VR.
Comparative Analysis of Similarity-Based Edge Construction Methods for Village Welfare Index Networks
This study compares three similarity-based edge construction methods for village welfare networks from household welfare attributes in Data Desa Presisi. Households are represented as nodes, while edges denote computational similarity between household welfare profiles rather than social relationships. Five Village Welfare Index dimensions were discretized and transformed into binary one-hot representations. Pairwise similarities were calculated using Cosine Similarity, Jaccard Index, and Pearson Correlation, followed by automatic thresholding to retain strong relationships. Network evaluation focused on the Largest Connected Component, while community structure was assessed using Louvain modularity across 14 villages. All methods produced analyzable networks, achieving a 100% success rate and 97.17% average node coverage. Jaccard achieved the highest mean modularity (0.5074) and win rate (71.43%; 10 of 14 villages). A Friedman test confirmed differences among methods (χ² = 8.71, p = 0.013). Holm-corrected Wilcoxon tests showed that Jaccard significantly outperformed Cosine and Pearson, whereas Cosine and Pearson did not differ significantly. These findings indicate that attribute-overlap-based edge construction provides most consistent representation of household welfare-profile proximity under the tested binary representation and automatic-thresholding scheme, while emphasizing that this advantage is context-dependent rather than universal.
An AI-Assisted Knowledge Management System Prototype for Fisheries Extension Officers: A Usability Evaluation
Fisheries extension officers accumulate tacit knowledge that is rarely documented and at risk of loss as senior officers retire, while existing systems lack structured capture and motivation mechanisms. This study develops and evaluates a Knowledge Management System (KMS) prototype integrating AI-guided narrative acquisition, knowledge mapping, and gamification to support tacit-knowledge externalization. Using PHP, MySQL, and Google Gemini 1.5 Flash, spoken field narratives are structured via the STAR framework under human review, visualized through a weighted expertise graph, and reinforced through a Self-Determination-Theory-based motivational mechanism. Ten fisheries extension officers (five senior, five junior) evaluated the prototype through scenario-based testing, the System Usability Scale, and interviews. Functional black-box testing confirmed that all six core scenarios (authentication, voice-based STAR extraction, moderation, knowledge mapping, gamification, and messaging) executed correctly (6/6, 100% pass rate), and the prototype achieved a mean SUS score of 83.5 (excellent), with qualitative findings indicating reduced perceived documentation burden and increased motivation, alongside dialect-recognition and internet-dependency limitations. This integrated prototype architecture demonstrates preliminary perceived effectiveness rather than validated organizational impact, given the small sample and short-term evaluation.
AI-Assisted Knowledge Management System (KMS)
Evaluating E-Commerce User Satisfaction Using an Integrated PIECES and EUCS Framework
E-commerce in Indonesia is growing rapidly because of the increase in internet users and digital commerce activities. This study evaluates user satisfaction with e-commerce applications using a partial integration of the PIECES Framework and selected EUCS dimensions, including Performance, Information and Data, Economics, Control and Security, Efficiency, Service, Format, and Timeliness. Rather than implementing the complete EUCS model, this study incorporates only the selected dimensions considered most relevant to evaluating user satisfaction with e-commerce applications. A quantitative approach was employed using questionnaires distributed to 200 e-commerce users in Banyumas Regency through purposive sampling. The data were analyzed using SPSS, including validity and reliability tests, classical assumption tests, multiple linear regression, the coefficient of determination, and hypothesis testing. The regression model explained 56.4% of the variance in User Satisfaction (Adjusted R² = 0.564), indicating moderate explanatory power. The results show that Information and Data, Economics, Efficiency, Service, and Timeliness significantly influence user satisfaction, whereas Performance, Control and Security, and Format do not have significant effects. These findings provide practical guidance for e-commerce providers to prioritize improvements in information quality, transaction efficiency, customer service responsiveness, and system usability usabulity that are limited to respondents in Banyumas Regency.
Multimodal Emotion Classification of Indonesian Memes on Platform X Using IndoBERT and YOLOv11
The widespread use of social media, particularly Platform X, has increased the popularity of memes as a multimodal communication medium that combines textual and visual elements to express emotions, opinions, and social reactions. This study proposes a multimodal emotion classification approach for Indonesian-language memes by integrating IndoBERT for textual analysis and YOLOv11 operating in image classification mode for visual analysis through a weighted late fusion strategy. An initial dataset of 2,810 Indonesian-language memes was collected through web scraping. After removing corrupted or unreadable images, 2,547 valid samples remained. Each meme was manually annotated into one of six emotion categories—Happiness, Disgust, Anger, Sadness, Fear, and Surprise—based on the annotators' judgment of the dominant emotion conveyed by the combination of text and image, following Ekman's Basic Emotion framework. The dataset was divided using a stratified 80:10:10 split into 2,037 training, 255 validation, and 255 testing samples. The validation set was used to determine the optimal fusion weight, while the held-out test set was reserved exclusively for final evaluation. The best-performing weighted late fusion model (α = 0.6) achieved 72.55% accuracy, 73.03% macro precision, 72.76% macro recall, and 72.66% macro F1-score on the test set. Within the constructed dataset, the proposed multimodal approach outperformed the evaluated IndoBERT-only and YOLOv11-only baselines, indicating that combining textual and visual information can improve emotion classification performance for Indonesian-language meme content.
Reliability-Aware Public Issue Priority Mapping for Indonesia’s Free Nutritious Meal Program Using Sentiment Calibration and BERTopic
The Free Nutritious Meal Program (MBG) has generated extensive public discussion on platform X. This study develops a reliability-aware framework for mapping public issue priorities by integrating sentiment analysis, probability calibration, and topic modeling. A final dataset of 1,641 public X posts was de-identified, preprocessed, relevance-filtered, manually labeled, validated, and proportionally split. TF-IDF + SVM was used as a classical baseline, while IndoBERTweet was fine-tuned and calibrated using temperature scaling. BERTopic was applied to generate topics, followed by manual interpretation into substantive issue groups. TF-IDF + SVM outperformed IndoBERTweet, achieving 0.8138 accuracy and 0.7999 Macro-F1, while IndoBERTweet achieved 0.7611 accuracy and 0.7142 Macro-F1. IndoBERTweet was retained because it provides probability-based confidence scores for calibration and priority mapping. Calibration modestly reduced NLL from 0.5239 to 0.5199 and ECE from 0.0700 to 0.0682. BERTopic produced 19 non-noise topics with a coherence score of 0.4552. The highest-priority public discussion theme concerned food safety, poisoning-related discourse, and consumption quality. This framework provides initial public-opinion monitoring input, not definitive policy evaluation, and requires external validation before formal policy use.
Integrating IndoBERT-Based Sentiment Analysis and PSI-TOPSIS Into a Decision Support System for Convection Production Prioritization
Determining production priorities in the convection industry remains challenging due to the diversity of product alternatives and the complexity of operational and customer-related factors. This study develops a Decision Support System (DSS) by integrating IndoBERT-based customer sentiment analysis with Multi-Criteria Decision Making (MCDM) approaches to identify optimal production priorities at Kenisya Gallery. A total of 371 Shopee customer reviews were analyzed using IndoBERT to extract customer sentiment information, which was then integrated with eight operational criteria. The Preference Selection Index (PSI) method was applied to determine the importance weights of each criterion, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank 28 product alternatives. The results indicate that 84.4% of customer reviews expressed positive sentiment. Among the evaluation criteria, raw material availability (C8) achieved the highest PSI weight of 25.06%. The PSI-TOPSIS approach identified the Azella Black Series as the highest-priority product for production, achieving a closeness coefficient (Ci) value of 0.8730. This research demonstrates the effectiveness of combining customer sentiment analysis and MCDM techniques to support data-driven production planning. Nevertheless, this study is limited to a single case study and does not yet incorporate expert validation or evaluation of actual production performance.
Sentiment Analysis; IndoBERT; Preference Selection Index; TOPSIS; Production Priority.
Open Government Data-Based Smart Tourism Analytics: A Conceptual Governance Framework
Digital transformation and Open Government Data (OGD) offer opportunities for more transparent, evidence-based, and sustainable tourism governance. However, prior smart tourism and OGD studies remain fragmented, often addressing data publication, analytics, governance, visualization, or sustainability evaluation separately. This study develops an initial conceptual framework for OGD-based Smart Tourism Analytics to support sustainable destination governance. The method combines a structured Scopus-only search, PRISMA-based screening, Biblioshiny mapping, systematic literature synthesis, and layered framework derivation. Of 68 Scopus records published during 2021–2026, 32 documents were selected, comprising 19 core studies and 13 contextual evidence sources. The analysis identifies persistent gaps in OGD integration, analytics readiness, governance control, stakeholder-oriented visualization, and outcome evaluation. The proposed framework includes five layers: OGD integration, smart analytics, decision support and visualization, sustainable destination governance, and tourism outcome evaluation. Pangandaran is used only as an illustrative application context, not as empirical validation. No prototype or live API integration was tested. Accordingly, the framework remains conceptual and requires prototype development, live API integration, and field validation with local governments.
The Role of Facilitating Conditions and Social Influence in Users’ Behavioral Intention to Use Digital Tourism Information Systems
Digital tourism information systems have changed how travelers search for information and plan their trips. However, user acceptance still varies, especially in areas with limited internet access. This study examines the factors that influence users’ intention to use digital tourism information systems, focusing on facilitating conditions, social influence, perceived usefulness, and perceived ease of use. A digital survey was conducted with 144 students enrolled in information technology-related higher education programs, aged 17–21 years, who had more than three years of experience using digital tourism information systems. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that facilitating conditions have the strongest effect on behavioral intention, followed by social influence. In contrast, perceived usefulness and perceived ease of use do not have significant effects. These findings show a context-specific pattern in technology adoption, where facilitating conditions and social influence are more important for the respondents in this study. However, the findings should be interpreted within the context of this homogeneous sample of young, digitally experienced students and should not be generalized to all digital tourism users. The results also suggest that the importance of technology adoption factors may differ depending on user characteristics and the context of system use. This study measures behavioral intention, not actual or continued system use.
Residual Learning-Based Hybrid ARIMA–LSTM for Digital Retail Demand Forecasting
The rapid growth of digital retail has increased the need for accurate demand forecasting to support inventory planning and operational decision-making. This study proposes and evaluates a residual learning-based Hybrid ARIMA–LSTM framework for daily retail demand forecasting. A univariate dataset containing 1,826 daily demand observations collected between January 2021 and December 2025 was preprocessed using data cleaning, Min–Max normalization, Augmented Dickey–Fuller (ADF) testing, first-order differencing, and sliding window transformation. ARIMA was employed to model the linear component of the demand series, while LSTM was used to learn nonlinear residual patterns. The proposed framework was evaluated against ARIMA, LSTM, Exponential Smoothing, Moving Average, and Naïve Forecast using MAE, MSE, and RMSE on the original demand scale after inverse normalization. The Hybrid ARIMA–LSTM achieved the lowest MSE (4668.71) and RMSE (68.32) among the evaluated forecasting models, whereas the standalone LSTM achieved a slightly lower MAE (49.87). Furthermore, residual analysis showed that the Hybrid ARIMA–LSTM produced stable forecasting performance, with residuals randomly distributed around the zero reference line, indicating minimal systematic prediction bias. This study demonstrates that a residual learning-based Hybrid ARIMA–LSTM framework can effectively improve daily digital retail demand forecasting by integrating statistical and deep learning models under identical experimental settings.
Optimizing KNN Classification for Heart Disease Prediction Using Sequential Forward Selection
Irrelevant attributes often degrade the effectiveness of distance-based algorithms like K-Nearest Neighbors (KNN) in heart disease prediction. This study enhances a KNN model using Sequential Forward Selection (SFS) on the Cleveland dataset to optimize computational efficiency and precision while maintaining stable recall. To rigorously prevent data leakage, data partitioning was executed prior to mode imputation and normalization, followed by feature selection within a 5-fold stratified cross-validation framework. To further guarantee model robustness and rule out arbitrary selection, a 5-repeated 10-fold cross-validation and a 50-iteration feature stability analysis were executed. Compared to a baseline model (k=7, Euclidean; 80.33% accuracy) utilizing all 13 original attributes, the optimal 7-feature subset (cp, trestbps, chol, thalach, oldpeak, ca, thal) reduced the dimensional space by 46% and achieved 81.97% Accuracy, 77.42% Precision, 85.71% Recall, 78.79% Specificity, 0.9183 ROC-AUC, and an 81.36% F1-Score on an independent hold-out test set comprising 61 samples. Although McNemar's test (p = 1.0000) indicated the absolute accuracy improvement was not statistically significant, the 7-feature model successfully eliminated unstable features and reduced statistical noise. Ultimately, applying SFS provides a highly efficient, computationally lightweight framework for heart disease prediction without compromising predictive reliability.
Machine Learning-Based Multi-Class Scholarship Classification with a Streamlit Prototype
The scholarship selection process at a private university is still conducted manually by reviewing scholarship applicant documents individually, resulting in a time-consuming process and potential inconsistencies in evaluation. This study aims to develop a multi-class Machine Learning-based classification model to support scholarship classification based on recipient criteria and evaluating model performance using accuracy, precision, recall, F1-score, and confusion matrix metrics. This study applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework consisting of six stages Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The dataset consists of 408 historical scholarship recipient records categorized into four scholarship classes. A key contribution of this study is the integration of the CRISP-DM methodology with feature importance analysis using Random Forest Feature Importances, class balancing using SMOTE, and machine learning classifiers, including Random Forest, SVM, and Naïve Bayes, within a unified predictive modelling framework. Based on the evaluation results, the Support Vector Machine algorithm achieved the best performance with an accuracy of 77% and a macro F1-score of 63%, followed by Random Forest at 76% and Naïve Bayes at 70%. The SVM model was then implemented as a Streamlit-based web to support scholarship recommendations and is not intended as a final scholarship approval system. This research contributes to the development of an efficient, data-driven scholarship classification support system for higher education institutions.
Hybrid CNN-RNN Architecture with MFCC-LFCC Features for Audio Deepfake Detection
The proliferation of sophisticated audio deepfake technology poses a significant threat to digital voice authentication and forensic verification systems. This research addresses this challenge by developing and evaluating a lightweight hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) architecture for audio deepfake detection. The proposed model integrates a CNN for spatial feature extraction with a bidirectional RNN for temporal dependency modeling, utilizing an early vertically fused feature set of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) stabilized via utterance-level Cepstral Mean and Variance Normalization (CMVN). We assessed the proposed framework on the official ASVspoof 2019 Logical Access (LA) evaluation benchmark dataset (71,237 trials). Comprehensive evaluation on the official evaluation set demonstrated promising performance within the evaluated benchmark, achieving a Global Equal Error Rate (EER) of 8.24% and an Area Under the Receiver Operating Characteristic (ROC-AUC) of 0.9680, while maintaining an internal validation EER of 0.33% on known attacks. While showing high sensitivity to bona fide speech and robust resilience against advanced Neural Text-to-Speech synthesis (EER < 0.25% for A07–A10), the framework exhibits notable vulnerability to phase-preserving voice conversion attacks. Consequently, without real-world forensic operational testing, the model serves as an initial diagnostic screening approach rather than a fully operational forensic solution, and still requires further cross-repository and noisy-condition validation.
Temporal Healthcare Data Warehouse with SCD Type 2 for OLAPBased Executive Information Systems: A Galaxy Schema Approach
Modern hospital information systems operate within fragmented and heterogeneous OLTP environments designed for transaction recording rather than strategic analytical workloads. This study designs and implements a temporal healthcare Data Warehouse for OLAP analytics supporting historical data consistency and multidimensional performance monitoring aligned with Indonesian Ministry of Health (Kemenkes RI) standards. The Kimball bottom-up methodology was applied to integrate two heterogeneous OLTP sources into a Galaxy Schema comprising two fact tables and seventeen dimension tables. The ETL pipeline was implemented using SSIS with a complete SCD Type 2 workflow preserving full patient dimension history across ward transfers and physician reassignments. The OLAP layer was constructed using SSAS Tabular with VertiPaq in-memory engine and DAX-based measures, visualized through a Power BI Executive Information System dashboard. Empirical validation using 52,321 discharged patient records over a one-year observation period demonstrated an average OLAP Speedup Ratio of 30.27× over equivalent relational SQL queries across ten benchmark runs, ranging from 2.98× (Slice) to 79.18× (Dice), with all response times below 40 ms and 100% computational accuracy across all six Kemenkes RI KPIs. The novelty lies in integrating a complete SCD Type 2 temporal model within a Galaxy Schema healthcare data warehouse, empirically validated against live heterogeneous hospital data and nationally aligned KPI standards.
Comparative Machine Learning Models for Predicting SME Business Performance
Small and Medium Enterprises (SMEs) play an essential role in supporting economic growth, employment creation, and innovation across developing and emerging economies. To maintain competitiveness, SMEs need to improve their business performance by understanding the factors that influence organizational success. Several factors, including risk-taking, open innovation, cost leadership, proactiveness, aggressiveness, and autonomy, contribute to SME performance improvement. This study aims to develop and compare machine learning models for predicting SME business performance using these influencing factors. A dataset containing 283 SME records obtained from the Mendeley Data Repository was used in this study. Four machine learning approaches were evaluated, including Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and ANN optimized using Particle Swarm Optimization (PSO). The models were assessed using regression performance metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and computational execution time. The experimental results indicate that RF achieved the best prediction performance with an R² of 0.924, RMSE of 0.120, and MAE of 0.065, demonstrating high predictive accuracy and low error. XGBoost and ANN-PSO also showed competitive performance, while ANN achieved moderate results. Therefore, RF is recommended as an effective model for SME performance prediction. Future studies should employ larger datasets and external validation to improve model generalizability.
A Web-Based Spatial Decision Support System For Stunting Risk Prediction Using Random Forest and STBM Data
Stunting mitigation requires precise interventions, yet local health centers frequently face fragmented data. This study develops a preliminary WebGIS-based decision-support prototype for stunting risk prediction to facilitate targeted resource allocation. Utilizing the CRISP-DM methodology, we implemented a direct cross-region model deployment. A Random Forest classifier, trained on a feature-complete perinatal dataset (n = 78) from a source village, was deployed to a target domain integrating 83 toddler records and 10,113 household-level STBM environmental records in Ngawen District. The model achieved an overall accuracy of 81.93% and a weighted F1-score of 0.817. Class-specific F1-scores reached 0.906 (Normal), 0.744 (Mild), 0.757 (Moderate), and 0.818 (Severe). Feature importance analysis identified Birth Weight, Birth Length, and the aggregated village-level STBM score as primary predictors. Furthermore, spatial analysis revealed predicted high-risk clusters in Sarimulyo (5.58%) and Gondang (5.15%), demonstrating an inverse relationship between sanitation coverage and stunting severity. However, these spatial findings are based on model predictions and aggregated indicators rather than confirmed causal relationships. Due to the limited sample size and the prototype's untested status with end-users, broader external validation, clinical verification, and formal usability testing are essential before operational deployment.
Digital Twin and AI in Marine Systems: A Bibliometric Analysis from an Information Systems Perspective
Marine Digital Twin (DT) and Artificial Intelligence (AI) research spans maritime engineering, environmental monitoring, and governance contexts. This study provides a Scopus-based bibliometric and scientometric mapping of 289 English-language documents published from 2020 through April 10, 2026. VOSviewer and the Bibliometrix R-package were used for keyword co-occurrence mapping and descriptive analysis. Annual output was 4, 9, 24, 37, 50, 108, and 57 documents for 2020-2026, respectively; 2026 is a partial-year observation. China (n = 78), the United Kingdom (n = 31), and the United States (n = 27) were the leading countries. University College London (n = 10), Ningbo University (n = 8), and Dr. D. Y. Patil Institute of Technology (n = 7) had the highest affiliation counts. Three clusters were identified consistently: Industrial Maritime Applications, AI and Algorithmic Methods, and Environmental Monitoring and Earth Systems. The corpus remains dominated by engineering and offshore infrastructure, while the smaller environmental cluster does not by itself demonstrate a transition toward conservation. The Information Systems contribution is framed through data governance, semantic interoperability, platform ecosystems, and decision-support systems.
A Modular Agroclimatic Feature Engineering Framework for Country-Level Crop Yield Prediction Using XGBoost and LightGBM
Accurate crop yield prediction is essential for supporting food-security assessment, agricultural planning, and policy-level decision-making. This study proposes a modular agroclimatic feature engineering framework for country-level crop yield prediction using XGBoost and LightGBM. The proposed framework integrates climate indicators, climate–pesticide interactions, temporal descriptors, composite agroclimatic indices, and nonlinear transformations to improve predictive representation while controlling data leakage. Experiments were conducted on 28,151 country–crop–year observations from 98 countries covering 1990–2013. To evaluate temporal and geographic generalization, models were assessed using time-based validation, Random KFold, GroupKFold by country, bootstrap confidence intervals, and held-out-country evaluation. Results show that LightGBM with the S3 feature configuration achieved the best temporal prediction performance, obtaining an R² of 0.9492 and RMSE of 21,257 hg/ha. However, held-out-country evaluation revealed lower transferability, with the best configuration achieving R² of 0.6694, highlighting the challenge of geographic generalization. The findings demonstrate that engineered agroclimatic features can significantly improve country-level crop yield prediction, but model performance depends strongly on the validation setting. This framework provides a reliable benchmark for leakage-controlled agricultural machine learning research rather than direct farm-level operational forecasting.
Sentiment Analysis of Public Opinion on The Plastic Waste Issue on Social Media X Using TF-IDF, Naïve Bayes, and SVM
Plastic waste has become a critical environmental challenge due to increasing consumption patterns and inadequate waste management practices. Understanding public perceptions of plastic waste issues is essential for supporting environmental awareness and policy development. Social Media X provides a large-scale and real-time source of public opinions that can be analyzed through sentiment analysis techniques. This study aims to identify public sentiment trends regarding plastic waste issues on Social Media X and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms using TF-IDF feature extraction. The research applies the CRISP-DM framework, including data understanding, data preparation, preprocessing, modeling, evaluation, and visualization stages. A total of 7,277 Indonesian-language posts were collected through web scraping, with 7,275 posts retained after data preparation. The preprocessing process consisted of cleansing, case folding, tokenization, stopword removal, normalization, and lexicon-based sentiment labeling. The dataset was classified into three sentiment categories: positive, negative, and neutral. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, Macro F1-score, Balanced Accuracy, and Stratified 5-Fold Cross-Validation. The results show that SVM achieved better performance than Naïve Bayes, with an accuracy of 83.64%, Macro F1-score of 79.00%, and Balanced Accuracy of 75.94%. These findings indicate that SVM is more effective for sentiment classification of Indonesian plastic waste discussions using TF-IDF-based text representation.
Explainable XGBoost-Based Prediction of Student Reading Interest Using Digital Learning and Reading Behavior Indicators
Reading interest is an important indicator of academic engagement and lifelong learning, yet its relationship with digital learning behavior remains underexplored in higher education. This study proposes an explainable machine learning framework to predict student reading interest using digital learning behavior, reading habits, and demographic characteristics. Data were collected through self-reported questionnaires from 300 Indonesian university students. Reading interest was formulated as a binary target variable, and three feature scenarios were evaluated: digital behavior only (S1), reading behavior only (S2), and an integrated model combining all features (S3). The dataset was divided using an 80:20 stratified train–test split, with SMOTE applied only to the training data to address class imbalance while preventing information leakage. S3 achieved the highest ROC-AUC (91.92%) and cross-validation F1-score (86.62% ± 3.34%), while S1 obtained the same test accuracy (84.29%) and a slightly higher test F1-score (85.33%). SHAP analysis provided interpretability by identifying daily reading duration and monthly book count as key predictors. These findings indicate that multidimensional behavioral indicators can support early student-engagement screening systems. However, results from a single institutional context require cautious interpretation.
Comparative Classification of Promotional Sources in Higher Education Admissions Using K-Nearest Neighbor and Naive Bayes
This study evaluates and compares the performance of Naive Bayes and K-Nearest Neighbor (KNN) algorithms for classifying promotional-source categories in higher education admissions based on ten years of historical admission records. The objective is to analyze the capability of machine learning approaches in identifying patterns of applicant acquisition sources and to provide insights for institutional data-driven evaluation. After data preprocessing and quality filtering, 2,618 out of 4,901 records with complete target-variable information were retained and classified into seven promotional-source categories. Both algorithms were assessed using 5-fold cross-validation with multiple evaluation measures, including accuracy, macro-averaged recall, and comparison against a majority-class baseline to address the effect of severe class imbalance. Experimental results indicate that KNN achieved substantially higher overall accuracy (82.24%) than Naive Bayes (43.74%). However, neither model surpassed the majority-class baseline, demonstrating that accuracy alone can lead to misleading conclusions in highly imbalanced classification problems. In contrast, Naive Bayes obtained higher macro recall (36.32% compared with 18.14% for KNN), indicating a broader capability in recognizing minority promotional-source categories. The findings emphasize the importance of imbalance-aware evaluation and provide analytical insights into historical promotional-source distributions to support strategic admission planning and future institutional decision-making.
Rule-Based Aspect Extraction and IndoBERT-Based Sentiment Classification of Ruparupa Mobile Application Reviews
This study evaluates a pipeline that separates rule-based aspect extraction from IndoBERT-based binary sentiment classification for Indonesian Ruparupa mobile application reviews. Google Play reviews were collected on 31 July 2026, anonymized, deduplicated before aspect expansion, and cleaned by lowercasing, removing URLs, emails, and special characters, and normalizing whitespace; no slang normalization, stop-word removal, or stemming was applied. Ratings 1-2 and 4-5 provided weak negative and positive labels, while three-star reviews were excluded. A 331-entry aspect dictionary mapped 1,495 unique reviews into 2,873 aspect-review pairs across six aspects. Across five repeated leakage-free group hold-out splits, IndoBERT achieved mean accuracy 0.9179 ± 0.0214, macro F1 0.9178 ± 0.0214, and ROC-AUC 0.9719 ± 0.0103; a calibrated TF-IDF + linear SVM baseline achieved 0.8765 ± 0.0124, 0.8759 ± 0.0127, and 0.9452 ± 0.0102, respectively. A McNemar test on run 1 showed a significant paired difference (p = 0.00013). Performance measures agreement with rating-derived weak labels rather than human-validated aspect sentiment. Because results from system-assigned aspects lacked independent human validation, aspect frequencies are descriptive rule-system outputs. Within this dataset, IndoBERT performed consistently across the five splits; supervised aspect extraction and human aspect-level annotation remain priorities.
Performance Trade-Off Analysis of Faster R-CNN with Grid-Based Histogram for Student Face Detection
Face detection supports applications such as security, identity management, human–computer interaction, and academic information systems. Faster R-CNN is known for strong detection accuracy, but its Region Proposal Network produces many candidate boxes, including background proposals, which may increase processing cost. This study replaces the conventional anchor-generation process with a Grid-Based Histogram method to improve inference efficiency while retaining competitive detection performance. Experiments were conducted on 1132 annotated student profile images collected from the Campus Information System of Institut Teknologi Del. The standard and modified models were evaluated using mean Intersection over Union (IoU), mean Average Precision at IoU 0.50 (mAP@50), and average latency per image with an inference batch size of one. Standard Faster R-CNN achieved an IoU of 0.7595, an mAP@50 of 0.9818, and a latency of 0.170 s per image. The modified model obtained an IoU of 0.7519, an mAP@50 of 0.9719, and a latency of 0.147 s per image. Thus, latency decreased by about 13.53%, with small reductions in localization and detection accuracy. The novelty of this study lies in applying a Grid-Based Histogram as a lightweight replacement for conventional anchor generation in Faster R-CNN, resulting in a preliminary speed–accuracy trade-off rather than an overall performance improvement.
A Hybrid ACO-BPNN-XGBoost Model for Monthly Rainfall Time-Series Forecasting
This study aimed to develop and evaluate a hybrid forecasting model integrating Ant Colony Optimization (ACO), Backpropagation Neural Network (BPNN), and XGBoost for monthly rainfall prediction. The proposed hybrid framework combines optimization, neural network, and boosting techniques within a single forecasting model. Monthly rainfall time-series data from 2016 to 2025 in Alas Subdistrict, Sumbawa Regency, West Nusa Tenggara, were used, comprising 120 observations obtained from BPS, BMKG, and NASA POWER. The methodology included data preprocessing, an 80%–20% chronological training–testing split, model development, and performance evaluation using MSE, MAE, RMSE, MAPE, and R². The results indicated that all models experienced performance degradation during testing, suggesting overfitting and limited generalization capability. The testing RMSE values for ACO, BPNN, XGBoost, and the hybrid model were 108.41, 109.33, 109.21, and 106.08 mm, respectively. The corresponding testing MAPE values were 2836.5%, 2900.5%, 2073.9%, and 2602.2%, although these values should be interpreted cautiously because rainfall observations occasionally approached zero. While the hybrid model achieved the lowest testing RMSE, the improvement over the best individual model was modest, and all testing R² values remained negative, indicating weak generalization capability. Therefore, the findings should be regarded as preliminary, and further validation using larger datasets, exogenous climatic predictors, baseline forecasting methods, and more rigorous evaluation procedures is required.
Optimizing Multiclass Android Malware Family Classification Using SMOTE-Tomek Links and XGBoost
The increasing sophistication of Android malware attacks has created significant challenges for accurate malware family classification, particularly under highly imbalanced data distributions where minority malware families are frequently misclassified. This study presents a robust multiclass Android malware family classification framework by combining SMOTE-Tomek Links hybrid resampling with an optimized Extreme Gradient Boosting (XGBoost) classifier. The proposed framework addresses two critical issues in previous studies: ineffective handling of minority classes and potential data leakage during resampling and model validation. Experiments were conducted using the CCCS-CIC-AndMal-2020 After Reboot dataset containing 25,059 malware samples distributed across 14 malware families. The proposed approach applies stratified data partitioning, leakage-free SMOTE-Tomek Links integration within an imbalanced-learn pipeline, and RandomizedSearchCV-based hyperparameter optimization with 5-fold stratified cross-validation. Evaluation on an independent holdout test set demonstrates that the optimized framework achieves 80.09% accuracy, 79.85% weighted F1-score, 74.00% macro F1-score, and 97.48% OvR ROC-AUC, outperforming baseline XGBoost and Random Forest models. The results confirm that hybrid resampling combined with optimized gradient boosting improves classification reliability, especially in addressing severe class imbalance and enhancing recognition capability across diverse Android malware families.
Performance and Carbon Footprint Evaluation of a PWA-Based Waste Bank Information System
The increasing volume of unmanaged waste in Indonesia has encouraged the adoption of digital waste bank management systems. However, the environmental impact of digital transformation, particularly regarding energy consumption and carbon emissions, remains insufficiently explored in community-scale information systems. This study evaluates the performance and environmental efficiency of Sibanksa, a Progressive Web Application (PWA)-based Waste Bank Information System, based on Green IT principles. A quantitative descriptive approach was applied by measuring four indicators: system performance, data transfer efficiency, estimated energy consumption, and digital carbon footprint under cached and non-cached loading scenarios. The results demonstrate that service worker caching significantly reduced data transfer size from an average of 4.84 MB to 20.9 kB, decreased estimated energy consumption from 0.0000594 kWh to 0.0000547 kWh, and reduced estimated carbon emissions from 0.044456 g CO₂ to 0.040886 g CO₂, representing approximately 8% improvements. Nevertheless, the overall page size remained relatively high (8.12 MB without caching and 7.46 MB with caching), while mobile rendering performance indicators, including First Contentful Paint (FCP) and Largest Contentful Paint (LCP), did not satisfy Web Vitals recommendations. This study contributes a practical measurement framework that integrates web performance analysis, energy efficiency assessment, and digital carbon footprint estimation for waste bank management systems. The findings provide empirical insights for developing more sustainable PWA-based information systems aligned with Green IT principles.
Machine Learning for Software Deployment in the Public Sector: A Systematic Review and Research Agenda for African Contexts
Failures in deploying digital public services can disrupt essential systems and affect millions of citizens. Machine learning (ML)-based software deployment decision support, including build risk prediction, release gating, autoscaling, rollback assistance, and post-deployment anomaly detection, offers opportunities for safer and more reliable releases. However, the extent of existing evidence in public-sector environments, particularly within African institutions, remains unclear. Following the PRISMA 2020 guidelines, this systematic literature review searched five databases using predefined inclusion criteria and a six-item quality assessment. A total of 33 peer-reviewed studies published between 2018 and 2025 were included, while studies focusing exclusively on MLOps were excluded. The findings reveal that none of the reviewed studies (0/33) explicitly evaluated ML deployment decision support in public-sector contexts or African institutions; existing evidence originates primarily from private-sector or unspecified environments. Research efforts are concentrated on autoscaling (12/33, 36%) and build prediction (9/33, 27%), with tree-based models being the dominant approach (16/33, 48%). Furthermore, only one study (3%) reported statistical significance testing or confidence intervals. This review identifies a research and evidence gap rather than confirming the absence of practical adoption. It proposes a staged research agenda toward explainable, lightweight, and context-aware ML deployment support for African public institutions.
Toward AI-Based Child Emotion Early Warning Systems in Child-Oriented Digital Environments: A Socio-Technical Systematic Review
AI-based emotion recognition is gaining increasing attention in child-oriented digital environments, including digital play, AI toys, online learning, therapeutic platforms, and interactive child-computer systems. However, the translation of recognized emotions into responsible caregiver-facing early warnings remains insufficiently explored. This systematic literature review synthesizes technical and socio-technical perspectives on child emotion recognition, focusing on sensing modalities, AI approaches, datasets, alert interpretation, trust, privacy, ethics, governance, and caregiver acceptance. Following PRISMA 2020 guidelines, 400 records from Scopus and IEEE Xplore were screened, resulting in 32 studies included in the core synthesis. The findings reveal that facial-expression analysis and convolutional neural networks dominate current research, while child-specific datasets, multimodal learning, real-world validation, uncertainty communication, privacy-by-design, and caregiver-centered evaluation remain limited. This review proposes a socio-technical framework linking AI emotion inference with explainable alert translation, privacy-aware governance, and caregiver decision support. The proposed emotion-to-alert mechanism remains conceptual and requires empirical validation before practical deployment.
An Integrated XGBoost-SHAP Framework for XAI-based Analysis of Agroclimatic, Agronomic, and Operational Labor Proxy Affecting Block-Level Oil Palm Productivity
Oil palm block-level productivity (Yield Per Hectare/YPH) relies on complex agronomic, climatic, and operational interactions, yet existing predictive models often neglect internal estate conditions. We developed an XGBoost-SHAP framework using 1,351 block-year observations (2018–2024) from a single plantation in Central Kalimantan, Indonesia. Features include plant age, land area, rainfall, and a labor ratio functioning as a land-based workload proxy. The baseline random-split testing achieved R² = 0.6276, MAE = 2.2747 Tons/Ha, RMSE = 3.3303 Tons/Ha, and MAPE = 28.73%. However, evaluating the model under grouped block and time-based validation schemes revealed limited spatial and temporal transferability. SHAP analysis identified plant age (47.51%), land area (14.32%), annual rainfall (10.87%), and labor ratio (8.51%) as primary yield drivers, revealing non-linear interaction patterns. Rather than a fully autonomous tool, this framework provides a transparent interpretive heuristic and decision-support prototype to evaluate block-level operational strategies. By quantifying internal operational variables alongside agronomic factors, this study contributes valuable insights for precision agriculture, suggesting future work validate dashboard heuristics with estate managers and incorporate granular fertilization and harvesting logs to further enhance predictive robustness.
Reframing Technology Anxiety During Blended Learning Implementation: The Relationships among Perceived Safety, Social Influence, Satisfaction, and Behavioral Intention
This study examines the relationships among perceived safety, social influence, technology anxiety, user satisfaction, and behavioral intention in blended learning implementation, investigating whether technology anxiety can be interpreted as productive vigilance rather than merely a barrier to technology adoption. Based on the Transactional Theory of Stress and Coping, this research explores how institutional safeguards and social expectations influence technology anxiety among teachers in high-stakes educational settings. A quantitative cross-sectional approach was applied using purposive sampling involving 205 high school teachers in North Sumatra, Indonesia. Data were analyzed using PLS-SEM to assess the structural model and mediation effects. The results indicate that Perceived Safety (β=0.526, t=8.17, p<0.001) and Social Influence (β=0.232, t=3.60, p<0.001) positively influence Technology Anxiety, explaining 48.0% of its variance. Technology Anxiety positively affects User Satisfaction (β=0.774, t=17.40, p<0.001), which subsequently influences Behavioral Intention (β=0.503, t=4.92, p<0.001). The direct effect of Technology Anxiety on Behavioral Intention is insignificant (β=-0.167, t=1.63, p=0.104), while the indirect effect through User Satisfaction is significant (βindirect=0.389, p<0.001). These findings highlight that technology anxiety may represent heightened attentiveness associated with satisfaction rather than resistance, requiring further validation through longitudinal and experimental studies.
Residual-Based Hybrid ARIMA–Prophet for Medium Rice Price Forecasting in Kudus City
Rice prices as a primary food commodity in Indonesia often fluctuate, affecting food-price stability and public welfare. This study analyzes and forecasts Medium I and Medium II rice prices in Kudus City using ARIMA, Prophet, Hybrid ARIMA–Prophet, Random Forest, and benchmark models. Daily PIHPS price data from 2021–2025 were divided chronologically into 781 training observations from 2021–2023 and 523 testing observations from 2024–2025. Model performance was evaluated using MAE, RMSE, MAPE, R², and directional metrics. Hybrid ARIMA–Prophet achieved MAE of Rp369.68 and MAPE of 2.62% for Medium I, and MAE of Rp467.31 and MAPE of 3.38% for Medium II. However, simpler benchmark models achieved lower price-level errors than the hybrid model. Random Forest showed higher directional Accuracy than Hybrid ARIMA–Prophet, but its low Precision and F1-Score indicate that Accuracy should be interpreted cautiously. The directional analysis also showed a highly imbalanced distribution, with unchanged movements accounting for approximately 94% of test observations. The Diebold–Mariano test, applied only to the regression forecast errors of Hybrid ARIMA–Prophet and Random Forest, showed significant differences for both Medium I and Medium II (p < 0.001). Overall, the findings indicate that more complex models do not necessarily outperform simpler benchmarks for the studied rice-price series.