Journal of Information Systems and Informatics
type: channel
description: Journal of Information System and Informatics
publisher: Asosiasi Doktor Sistem Informasi Indonesia
language: en
publication name: Journal of Information Systems and Informatics
issn: 2656-5935
copyright: Authors Declaration The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
items: Antecedents of Citizen-Centric E-Government Adoption: A Systematic Literature Review and Future Research Agenda
items: AI-Enabled E-Learning in Correctional Centres: A Systematic Literature Review
items: Association Rule Mining for Uncovering Co-occurring Skill Sets in Multi-Portal ICT Job Advertisements
items: Machine Learning Models for Early Gingival Health Screening Using Routine Clinical and Behavioral Data
items: Comparative Sentiment Analysis of Profits Anywhere Application Reviews Using KNN and Naïve Bayes
items: Securing Educational Financial Data Against Insider Threats: A Hybrid Blockchain Approach with Merkle Tree Aggregation
items: Experimental Evaluation of Avalanche Effect and Hash Consistency in SHA-256, SHA3-256, and BLAKE2b-256 for Smart Grid Data Integrity Verification
items: A Temporal Patient Risk Timeline Framework for Longitudinal Clinical Deterioration Surveillance Using Hospital Data Warehouse and Machine Learning
items: Green Software Engineering for Sustainable IT Services: A Scopus-Based Systematic Literature Review and Bibliometric Mapping
items: An Explainable PCA-XGBoost Model for Predicting Bloodstream Infection in Hemodialysis Patients
items: A Socio-Technical Assessment of Information Security Management Using KAMI Index, ISO/IEC 27001:2022, and User Awareness
items: Comparative Analysis of Four Machine Learning Classifiers for Indonesian Hoax News Detection
items: User-Centered Design of Marker-Based Augmented Reality Learning Media to Improve Informatics Learning Effectiveness
items: User Acceptance of E-Kinerja for Employee Additional Income Policy: A TAM Study in Central Lombok
items: Interpretable Feature-Scenario Analysis for Ethereum Transaction Anomaly Detection Using Random Forest and XGBoost
items: Formulating Application Portfolios from IS/IT Strategy and Business Processes: A Systematic Literature Review
items: Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction
items: Application-Driven Parallel Differential Evolution: A Systematic Mapping Review of Scalable Optimization Applications
items: Extending the Technology Acceptance Model with Perceived Immersive Learning for VR Usage in Higher Education
items: Comparative Analysis of Similarity-Based Edge Construction Methods for Village Welfare Index Networks
items: An AI-Assisted Knowledge Management System Prototype for Fisheries Extension Officers: A Usability Evaluation
items: Evaluating E-Commerce User Satisfaction Using an Integrated PIECES and EUCS Framework
items: Multimodal Emotion Classification of Indonesian Memes on Platform X Using IndoBERT and YOLOv11
items: Reliability-Aware Public Issue Priority Mapping for Indonesia’s Free Nutritious Meal Program Using Sentiment Calibration and BERTopic
items: Integrating IndoBERT-Based Sentiment Analysis and PSI-TOPSIS Into a Decision Support System for Convection Production Prioritization
items: Open Government Data-Based Smart Tourism Analytics: A Conceptual Governance Framework
items: The Role of Facilitating Conditions and Social Influence in Users’ Behavioral Intention to Use Digital Tourism Information Systems
items: Residual Learning-Based Hybrid ARIMA–LSTM for Digital Retail Demand Forecasting
items: Optimizing KNN Classification for Heart Disease Prediction Using Sequential Forward Selection
items: Machine Learning-Based Multi-Class Scholarship Classification with a Streamlit Prototype
items: Hybrid CNN-RNN Architecture with MFCC-LFCC Features for Audio Deepfake Detection
items: Temporal Healthcare Data Warehouse with SCD Type 2 for OLAPBased Executive Information Systems: A Galaxy Schema Approach
items: Comparative Machine Learning Models for Predicting SME Business Performance
items: A Web-Based Spatial Decision Support System For Stunting Risk Prediction Using Random Forest and STBM Data
items: Digital Twin and AI in Marine Systems: A Bibliometric Analysis from an Information Systems Perspective
items: A Modular Agroclimatic Feature Engineering Framework for Country-Level Crop Yield Prediction Using XGBoost and LightGBM
items: Sentiment Analysis of Public Opinion on The Plastic Waste Issue on Social Media X Using TF-IDF, Naïve Bayes, and SVM
items: Explainable XGBoost-Based Prediction of Student Reading Interest Using Digital Learning and Reading Behavior Indicators
items: Comparative Classification of Promotional Sources in Higher Education Admissions Using K-Nearest Neighbor and Naive Bayes
items: Rule-Based Aspect Extraction and IndoBERT-Based Sentiment Classification of Ruparupa Mobile Application Reviews
items: Performance Trade-Off Analysis of Faster R-CNN with Grid-Based Histogram for Student Face Detection
items: A Hybrid ACO-BPNN-XGBoost Model for Monthly Rainfall Time-Series Forecasting
items: Optimizing Multiclass Android Malware Family Classification Using SMOTE-Tomek Links and XGBoost

Antecedents of Citizen-Centric E-Government Adoption: A Systematic Literature Review and Future Research Agenda
type: item
description: 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.
subject: Articles
  topic: section
subject: citizen-centric e-government adoption
  topic: keywords
subject: adoption antecedents
  topic: keywords
subject: UTAUT
  topic: keywords
subject: trust
  topic: keywords
subject: systematic literature review
  topic: keywords
creator: Kazembe Kazembe
creator: Obert Sifile
creator: Gilbert Mahlangu
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 3998
ending page: 4044
doi: 10.63158/journalisi.v8i4.1613

AI-Enabled E-Learning in Correctional Centres: A Systematic Literature Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Correctional Education
  topic: keywords
subject: AI-Enabled Learning
  topic: keywords
subject: Adaptive Learning
  topic: keywords
subject: Systematic Literature Review
  topic: keywords
creator: Molefe Maloma
creator: Malusi Sibiya
creator: James Chibueze
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4045
ending page: 4073
doi: 10.63158/journalisi.v8i4.1538

Association Rule Mining for Uncovering Co-occurring Skill Sets in Multi-Portal ICT Job Advertisements
type: item
description: 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.
subject: Articles
  topic: section
subject: Association Rule Mining
  topic: keywords
subject: ICT Skills
  topic: keywords
subject: Job Advertisements
  topic: keywords
subject: Labour Market Analytics
  topic: keywords
subject: Web Scraping
  topic: keywords
subject: Text Mining
  topic: keywords
subject: Apriori Algorithm
  topic: keywords
creator: Erick Samwi
creator: George Matto
creator: Mercy Mbise
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4074
ending page: 4101
doi: 10.63158/journalisi.v8i4.1671

Machine Learning Models for Early Gingival Health Screening Using Routine Clinical and Behavioral Data
type: item
description: 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.
subject: Articles
  topic: section
subject: Gingival Health Screening
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Multiclass Classification
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
subject: Periodontal Disease
  topic: keywords
creator: Ozzy Secio Riza
creator: Novia Lestari
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4102
ending page: 4130
doi: 10.63158/journalisi.v8i4.1640

Comparative Sentiment Analysis of Profits Anywhere Application Reviews Using KNN and Naïve Bayes
type: item
description: 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.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: K-Nearest Neighbor
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Fintech Application
  topic: keywords
subject: Google Play Store Reviews
  topic: keywords
creator: Yurindra
creator: Chandra Kirana
creator: Dian Novianto
creator: Tri Sugihartono
creator: Muhammad Ilham
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4131
ending page: 4158
doi: 10.63158/journalisi.v8i4.1658

Securing Educational Financial Data Against Insider Threats: A Hybrid Blockchain Approach with Merkle Tree Aggregation
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid Blockchain
  topic: keywords
subject: Merkle Tree Aggregation
  topic: keywords
subject: Insider Threats
  topic: keywords
subject: Insider Threat Detection
  topic: keywords
subject: Tamper-Evident Logging
  topic: keywords
subject: Reversal Entry
  topic: keywords
subject: Smart Contract
  topic: keywords
creator: Adi Alfian Hafis
creator: Soiful Hadi
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4159
ending page: 4183
doi: 10.63158/journalisi.v8i4.1719

Experimental Evaluation of Avalanche Effect and Hash Consistency in SHA-256, SHA3-256, and BLAKE2b-256 for Smart Grid Data Integrity Verification
type: item
description: 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.
subject: Articles
  topic: section
subject: Smart Grid
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Cryptographic Hash Function
  topic: keywords
subject: SHA-256
  topic: keywords
subject: SHA3-256
  topic: keywords
subject: BLAKE2b-256
  topic: keywords
subject: Avalanche Effect
  topic: keywords
creator: Muhammad Ridwan
creator: Rusydi Umar
creator: Muhammad Kunta Biddinika
creator: Puguh Wahyu Prasetyo
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4184
ending page: 4210
doi: 10.63158/journalisi.v8i4.1685

A Temporal Patient Risk Timeline Framework for Longitudinal Clinical Deterioration Surveillance Using Hospital Data Warehouse and Machine Learning
type: item
description: 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…
subject: Articles
  topic: section
subject: clinical deterioration
  topic: keywords
subject: ICU transfer
  topic: keywords
subject: temporal monitoring
  topic: keywords
subject: XGBoost
  topic: keywords
subject: retrospective dashboard prototype
  topic: keywords
creator: Herwanto
creator: Dian Nugraha
creator: Popy Yuniar
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4211
ending page: 4238
doi: 10.63158/journalisi.v8i4.1680

Green Software Engineering for Sustainable IT Services: A Scopus-Based Systematic Literature Review and Bibliometric Mapping
type: item
description: 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.
subject: Articles
  topic: section
subject: Green Software Engineering
  topic: keywords
subject: Sustainable IT Services
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Systematic Literature Review
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
creator: Cecep Muhamad Sidik Ramdani
creator: I Made Oka Widyantara
creator: Linawati
creator: Nyoman Putra Sastra
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4239
ending page: 4269
doi: 10.63158/journalisi.v8i4.1672

An Explainable PCA-XGBoost Model for Predicting Bloodstream Infection in Hemodialysis Patients
type: item
description: 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.
subject: Articles
  topic: section
subject: Bloodstream Infection
  topic: keywords
subject: Hemodialysis
  topic: keywords
subject: XGBoost
  topic: keywords
subject: PCA
  topic: keywords
subject: SHAP
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Rani Zulaikha
creator: Budi Warsito
creator: Aris Sugiharto
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4270
ending page: 4291
doi: 10.63158/journalisi.v8i4.1694

A Socio-Technical Assessment of Information Security Management Using KAMI Index, ISO/IEC 27001:2022, and User Awareness
type: item
description: 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.
subject: Articles
  topic: section
subject: KAMI Index
  topic: keywords
subject: ISO 27001:2022
  topic: keywords
subject: IT Awareness
  topic: keywords
subject: Socio-Technical System
  topic: keywords
subject: ISMS
  topic: keywords
creator: Nur Wachid Hidayatulloh
creator: Dinar Mutiara Kusumo Nugraheni
creator: Oky Dwi Nurhayati
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4292
ending page: 4322
doi: 10.63158/journalisi.v8i4.1746

Comparative Analysis of Four Machine Learning Classifiers for Indonesian Hoax News Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: hoax detection
  topic: keywords
subject: machine learning
  topic: keywords
subject: Indonesian language
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Bag-of-Words
  topic: keywords
creator: Dedi Irawan
creator: Sudarmaji
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4323
ending page: 4342
doi: 10.63158/journalisi.v8i4.1710

User-Centered Design of Marker-Based Augmented Reality Learning Media to Improve Informatics Learning Effectiveness
type: item
description: 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.
subject: Articles
  topic: section
subject: Augmented Reality
  topic: keywords
subject: User-Centered Design
  topic: keywords
subject: Marker-Based Tracking
  topic: keywords
subject: Learning Media
  topic: keywords
subject: Informatics
  topic: keywords
creator: Chairunnisa Umi Kalsum
creator: Ali Ibrahim
creator: Ken Ditha Tania
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4343
ending page: 4365
doi: 10.63158/journalisi.v8i4.1704

User Acceptance of E-Kinerja for Employee Additional Income Policy: A TAM Study in Central Lombok
type: item
description: 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.
subject: Articles
  topic: section
subject: Technology Acceptance Model (TAM)
  topic: keywords
subject: e-Kinerja
  topic: keywords
subject: Employee Additional Income (TPP)
  topic: keywords
subject: PLS-SEM.
  topic: keywords
subject: Public Sector Information Systems
  topic: keywords
creator: Baiq Amelia Suroyani
creator: Sofiansyah Fadli
creator: Baiq Yulia Fitriyani
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4366
ending page: 4399
doi: 10.63158/journalisi.v8i4.1725

Interpretable Feature-Scenario Analysis for Ethereum Transaction Anomaly Detection Using Random Forest and XGBoost
type: item
description: 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.
subject: Articles
  topic: section
subject: Anomaly Detection
  topic: keywords
subject: Ethereum Transaction Analysis
  topic: keywords
subject: LIME
  topic: keywords
subject: Random Forest
  topic: keywords
subject: SHAP
  topic: keywords
creator: Indana Zulfa
creator: Kusnawi
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4400
ending page: 4424
doi: 10.63158/journalisi.v8i4.1727

Formulating Application Portfolios from IS/IT Strategy and Business Processes: A Systematic Literature Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Systematic Literature Review
  topic: keywords
subject: Application Portfolio
  topic: keywords
subject: IS/IT Strategy
  topic: keywords
subject: Business Process
  topic: keywords
creator: Belva Rizki Mufidah
creator: Sholiq
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4425
ending page: 4460
doi: 10.63158/journalisi.v8i4.1737

Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid Machine Learning
  topic: keywords
subject: Class Imbalance
  topic: keywords
subject: Employee Turnover
  topic: keywords
subject: Turnover Prediction
  topic: keywords
subject: HR Analytics
  topic: keywords
creator: Rudi Setiawan
creator: Gatot Tri Pranoto
creator: Zed Abdullah
creator: Satria Abadi
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4461
ending page: 4495
doi: 10.63158/journalisi.v8i4.1732

Application-Driven Parallel Differential Evolution: A Systematic Mapping Review of Scalable Optimization Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Differential Evolution
  topic: keywords
subject: Parallel Computing
  topic: keywords
subject: GPU Computing
  topic: keywords
subject: Distributed Optimization
  topic: keywords
subject: Scalability
  topic: keywords
creator: Said Iskandar Al Idrus
creator: Rudi Setiawan
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4496
ending page: 4521
doi: 10.63158/journalisi.v8i4.1695

Extending the Technology Acceptance Model with Perceived Immersive Learning for VR Usage in Higher Education
type: item
description: 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.
subject: Articles
  topic: section
subject: Technology Acceptance Model
  topic: keywords
subject: Perceived Immersive Learning
  topic: keywords
subject: Virtual Reality
  topic: keywords
subject: Behavioral Intention
  topic: keywords
subject: Higher Education
  topic: keywords
creator: Ajub Ajulian Zahra Macrina
creator: Iwan Setiawan
creator: Darjat
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4522
ending page: 4562
doi: 10.63158/journalisi.v8i4.1696

Comparative Analysis of Similarity-Based Edge Construction Methods for Village Welfare Index Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Data Desa Presisi
  topic: keywords
subject: Edge Construction
  topic: keywords
subject: Household Similarity Network
  topic: keywords
subject: Jaccard Index
  topic: keywords
subject: Louvain Modularity
  topic: keywords
creator: Abdullah Alhayad Arafah
creator: Annisa
creator: Sofyan Sjaf
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4563
ending page: 4602
doi: 10.63158/journalisi.v8i4.1769

An AI-Assisted Knowledge Management System Prototype for Fisheries Extension Officers: A Usability Evaluation
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-Assisted Knowledge Management System (KMS)
  topic: keywords
subject: Knowledge Mapping
  topic: keywords
subject: Large Language Model (LLM)
  topic: keywords
subject: STAR Framework
  topic: keywords
subject: Tacit Knowledge Externalization
  topic: keywords
creator: Dani Saepuloh
creator: Irman Hermadi
creator: Yani Nurhadryani
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4603
ending page: 4630
doi: 10.63158/journalisi.v8i4.1817

Evaluating E-Commerce User Satisfaction Using an Integrated PIECES and EUCS Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: End-User Computing Satisfaction
  topic: keywords
subject: E-Commerce Applications
  topic: keywords
subject: Multiple Linear Regression
  topic: keywords
subject: PIECES Framework
  topic: keywords
subject: User Satisfaction
  topic: keywords
creator: Retno Waluyo
creator: Jeffri Prayitno Bangkit Saputra
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4631
ending page: 4655
doi: 10.63158/journalisi.v8i4.1716

Multimodal Emotion Classification of Indonesian Memes on Platform X Using IndoBERT and YOLOv11
type: item
description: 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.
subject: Articles
  topic: section
subject: Multimodal Emotion Classification
  topic: keywords
subject: Indonesian Memes
  topic: keywords
subject: IndoBERT
  topic: keywords
subject: YOLOv11
  topic: keywords
subject: Weighted Late Fusion
  topic: keywords
creator: Pungkas Subarkah
creator: Esti Widianti
creator: Agus Pramono
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4656
ending page: 4683
doi: 10.63158/journalisi.v8i4.1742

Reliability-Aware Public Issue Priority Mapping for Indonesia’s Free Nutritious Meal Program Using Sentiment Calibration and BERTopic
type: item
description: 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.
subject: Articles
  topic: section
subject: Probability Calibration
  topic: keywords
subject: BERTopic
  topic: keywords
subject: Public Issue Priority Mapping
  topic: keywords
subject: Policy Monitoring
  topic: keywords
subject: Free Nutritious Meal Program
  topic: keywords
creator: Rizaldi
creator: Dewi Anggraeni
creator: Abdul Kholiq
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4684
ending page: 4708
doi: 10.63158/journalisi.v8i4.1753

Integrating IndoBERT-Based Sentiment Analysis and PSI-TOPSIS Into a Decision Support System for Convection Production Prioritization
type: item
description: 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.
subject: Articles
  topic: section
subject: Sentiment Analysis; IndoBERT; Preference Selection Index; TOPSIS; Production Priority.
  topic: keywords
creator: Vitra Surya Ningrum
creator: Anteng Widodo
creator: Noor Latifah
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4709
ending page: 4730
doi: 10.63158/journalisi.v8i4.1809

Open Government Data-Based Smart Tourism Analytics: A Conceptual Governance Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: Bibliometric Analysis
  topic: keywords
subject: Conceptual Framework
  topic: keywords
subject: Open Government Data
  topic: keywords
subject: Smart Tourism Analytics
  topic: keywords
subject: Sustainable Destination Governance
  topic: keywords
subject: Tourism Governance
  topic: keywords
creator: Ucu Nugraha
creator: Murnawan
creator: Zakiah Darajat
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4731
ending page: 4757
doi: 10.63158/journalisi.v8i4.1718

The Role of Facilitating Conditions and Social Influence in Users’ Behavioral Intention to Use Digital Tourism Information Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital Tourism Information Systems
  topic: keywords
subject: Behavioral Intention
  topic: keywords
subject: Social Influence
  topic: keywords
subject: TAM
  topic: keywords
subject: UTAUT
  topic: keywords
creator: Candra Agustina
creator: Eka Rahmawati
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4758
ending page: 4781
doi: 10.63158/journalisi.v8i4.1750

Residual Learning-Based Hybrid ARIMA–LSTM for Digital Retail Demand Forecasting
type: item
description: 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.
subject: Articles
  topic: section
subject: Demand Forecasting
  topic: keywords
subject: ARIMA
  topic: keywords
subject: LSTM
  topic: keywords
subject: Hybrid ARIMA–LSTM
  topic: keywords
subject: Residual Learning
  topic: keywords
subject: Digital Retail
  topic: keywords
creator: Dwi Hartanti
creator: Aprilisa Arum Sari
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4782
ending page: 4810
doi: 10.63158/journalisi.v8i4.1706

Optimizing KNN Classification for Heart Disease Prediction Using Sequential Forward Selection
type: item
description: 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.
subject: Articles
  topic: section
subject: Heart Disease Prediction
  topic: keywords
subject: Sequential Forward Selection (SFS)
  topic: keywords
subject: K-Nearest Neighbors (KNN)
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Wrapper Feature Selection
  topic: keywords
creator: Herman
creator: Rusydi Umar
creator: Deni Kuswandani
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4811
ending page: 4835
doi: 10.63158/journalisi.v8i4.1744

Machine Learning-Based Multi-Class Scholarship Classification with a Streamlit Prototype
type: item
description: 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.
subject: Articles
  topic: section
subject: Scholarship Classification
  topic: keywords
subject: Multi-Class Classification
  topic: keywords
subject: Machine Learning Algorithms
  topic: keywords
subject: CRISP-DM
  topic: keywords
subject: Streamlit
  topic: keywords
creator: Siti Nabila Ariza Fitri
creator: Rona Nisa Sofia Amriza
creator: M. Yoka Fathoni
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-22
publication date: 2026-08-22
starting page: 4836
ending page: 4864
doi: 10.63158/journalisi.v8i4.1673

Hybrid CNN-RNN Architecture with MFCC-LFCC Features for Audio Deepfake Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: Audio Deepfake Detection
  topic: keywords
subject: Hybrid CNN-RNN
  topic: keywords
subject: MFCC
  topic: keywords
subject: LFCC
  topic: keywords
subject: Audio Spoofing Detection
  topic: keywords
subject: Deepfake Audio Forensics
  topic: keywords
creator: Muh. Hajar Akbar
creator: Nurfitria Ningsi
creator: Aldi
creator: Muhammad Na’im Al Jum’ah
creator: Ilcham
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 4865
ending page: 4893
doi: 10.63158/journalisi.v8i4.1723

Temporal Healthcare Data Warehouse with SCD Type 2 for OLAPBased Executive Information Systems: A Galaxy Schema Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: healthcare data warehouse
  topic: keywords
subject: executive information system
  topic: keywords
subject: OLAP
  topic: keywords
subject: SCD Type 2
  topic: keywords
subject: galaxy schema
  topic: keywords
creator: Muhammad Mukhlis Khoirudin
creator: Irman Hermadi
creator: Hari Agung Adrianto
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 4894
ending page: 4912
doi: 10.63158/journalisi.v8i4.1651

Comparative Machine Learning Models for Predicting SME Business Performance
type: item
description: 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.
subject: Articles
  topic: section
subject: Small Medium Enterprises
  topic: keywords
subject: Business Performance
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Prediction
  topic: keywords
creator: Azizah
creator: Rahmat Gernowo
creator: Budi Warsito
creator: Jumi
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 4913
ending page: 4941
doi: 10.63158/journalisi.v8i4.1705

A Web-Based Spatial Decision Support System For Stunting Risk Prediction Using Random Forest and STBM Data
type: item
description: 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.
subject: Articles
  topic: section
subject: Decision Support System
  topic: keywords
subject: Stunting Risk Prediction
  topic: keywords
subject: Random Forest
  topic: keywords
subject: STBM
  topic: keywords
subject: WebGIS
  topic: keywords
subject: Public Health Decision Support
  topic: keywords
subject: Cross-Region Deployment
  topic: keywords
creator: Septia Oviyanti
creator: Supriyono
creator: Diana Laily Fithri
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 4942
ending page: 4969
doi: 10.63158/journalisi.v8i4.1807

Digital Twin and AI in Marine Systems: A Bibliometric Analysis from an Information Systems Perspective
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Marine Systems
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
subject: Scientometric Mapping
  topic: keywords
subject: Information Systems
  topic: keywords
subject: Environmental Monitoring
  topic: keywords
creator: Aditya Lapu Kalua
creator: Mochamad Agung Wibowo
creator: Luther Alexander Latumakulita
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 4970
ending page: 4989
doi: 10.63158/journalisi.v8i4.1717

A Modular Agroclimatic Feature Engineering Framework for Country-Level Crop Yield Prediction Using XGBoost and LightGBM
type: item
description: 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.
subject: Articles
  topic: section
subject: Agroclimatic feature engineering
  topic: keywords
subject: Crop yield prediction
  topic: keywords
subject: LightGBM
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Geographic generalization
  topic: keywords
creator: Ledyvia Audiz Coranov
creator: Kusnawi
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 4990
ending page: 5022
doi: 10.63158/journalisi.v8i4.1762

Sentiment Analysis of Public Opinion on The Plastic Waste Issue on Social Media X Using TF-IDF, Naïve Bayes, and SVM
type: item
description: 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.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Plastic Waste
  topic: keywords
subject: Social Media X
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: Support Vector Machine
  topic: keywords
creator: Ary Kania Sya'diah
creator: Muhammad Arifin
creator: Pratomo Setiaji
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-29
publication date: 2026-08-29
starting page: 5023
ending page: 5045
doi: 10.63158/journalisi.v8i4.1782

Explainable XGBoost-Based Prediction of Student Reading Interest Using Digital Learning and Reading Behavior Indicators
type: item
description: 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.
subject: Articles
  topic: section
subject: Reading Interest
  topic: keywords
subject: Educational Data Mining
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: SHAP
  topic: keywords
subject: Digital Learning Behavior
  topic: keywords
creator: Delpiah Wahyuningsih
creator: Heki Aprianto
creator: Lili Indah Sari
creator: Wishnu Aribowo Probonegoro
creator: Sri Rahayu
creator: Serly Oktarina
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-30
publication date: 2026-08-30
starting page: 5046
ending page: 5070
doi: 10.63158/journalisi.v8i4.1698

Comparative Classification of Promotional Sources in Higher Education Admissions Using K-Nearest Neighbor and Naive Bayes
type: item
description: 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.
subject: Articles
  topic: section
subject: promotional-source classification
  topic: keywords
subject: higher education admission
  topic: keywords
subject: K-Nearest Neighbor
  topic: keywords
subject: Naive Bayes
  topic: keywords
subject: class imbalance
  topic: keywords
creator: Elly Yanuarti
creator: Sujono
creator: Djoko Soetarno
creator: Rahmat Sulaiman
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-30
publication date: 2026-08-30
starting page: 5071
ending page: 5092
doi: 10.63158/journalisi.v8i4.1689

Rule-Based Aspect Extraction and IndoBERT-Based Sentiment Classification of Ruparupa Mobile Application Reviews
type: item
description: 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.
subject: Articles
  topic: section
subject: IndoBERT
  topic: keywords
subject: Rule-Based Aspect Extraction
  topic: keywords
subject: Weak Labeling
  topic: keywords
subject: Google Play Review Mining
  topic: keywords
subject: E-Commerce Reviews
  topic: keywords
creator: Erina Setyawati
creator: Berlilana
creator: Dhanar Intan Surya Saputra
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-30
publication date: 2026-08-30
starting page: 5093
ending page: 5111
doi: 10.63158/journalisi.v8i4.1816

Performance Trade-Off Analysis of Faster R-CNN with Grid-Based Histogram for Student Face Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: Face Detection
  topic: keywords
subject: Faster R-CNN
  topic: keywords
subject: Region Proposal Network
  topic: keywords
subject: Grid-Based Histogram
  topic: keywords
subject: Inference Latency
  topic: keywords
subject: Object Detection
  topic: keywords
creator: Arie Satia Dharma
creator: Herimanto
creator: Ranty Deviana Siahaan
creator: Lamboy Albertson Sirait
creator: Luna Sweeta Pangaribuan
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-30
publication date: 2026-08-30
starting page: 5112
ending page: 5134
doi: 10.63158/journalisi.v8i4.1681

A Hybrid ACO-BPNN-XGBoost Model for Monthly Rainfall Time-Series Forecasting
type: item
description: 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.
subject: Articles
  topic: section
subject: Rainfall Forecasting
  topic: keywords
subject: Hybrid Model
  topic: keywords
subject: Ant Colony Optimization
  topic: keywords
subject: Backpropagation Neural Network
  topic: keywords
subject: XGBoost
  topic: keywords
creator: Syaharuddin
creator: Safaruddin
creator: Vera Mandailina
creator: Abdillah
creator: Mahsup
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-30
publication date: 2026-08-30
starting page: 5135
ending page: 5174
doi: 10.63158/journalisi.v8i4.1693

Optimizing Multiclass Android Malware Family Classification Using SMOTE-Tomek Links and XGBoost
type: item
description: 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.
subject: Articles
  topic: section
subject: Android malware classification
  topic: keywords
subject: SMOTE-Tomek Links
  topic: keywords
subject: XGBoost
  topic: keywords
subject: imbalanced learning
  topic: keywords
subject: multiclass classification
  topic: keywords
creator: Ali Nur Ikhsan
creator: Adam Prayogo Kuncoro
creator: Debby Ummul Hidayah
creator: Fajar Ramadhan
rights: Copyright (c) 2026 Journal of Information Systems and Informatics https://creativecommons.org/licenses/by/4.0
date: 2026-08-30
publication date: 2026-08-30
starting page: 5175
ending page: 5205
doi: 10.63158/journalisi.v8i4.1788