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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 881 Documents
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Interpretable Feature-Scenario Analysis for Ethereum Transaction Anomaly Detection Using Random Forest and XGBoost Indana Zulfa; Kusnawi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1727

Abstract

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.
Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction Rudi Setiawan; Gatot Tri Pranoto; Zed Abdullah; Satria Abadi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1732

Abstract

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.
Formulating Application Portfolios from IS/IT Strategy and Business Processes: A Systematic Literature Review Belva Rizki Mufidah; Sholiq
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1737

Abstract

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.
Multimodal Emotion Classification of Indonesian Memes on Platform X Using IndoBERT and YOLOv11 Pungkas Subarkah; Esti Widianti; Agus Pramono
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1742

Abstract

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.
Optimizing KNN Classification for Heart Disease Prediction Using Sequential Forward Selection Herman; Rusydi Umar; Deni Kuswandani
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1744

Abstract

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.
A Socio-Technical Assessment of Information Security Management Using KAMI Index, ISO/IEC 27001:2022, and User Awareness Nur Wachid Hidayatulloh; Dinar Mutiara Kusumo Nugraheni; Oky Dwi Nurhayati
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1746

Abstract

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.
The Role of Facilitating Conditions and Social Influence in Users’ Behavioral Intention to Use Digital Tourism Information Systems Candra Agustina; Eka Rahmawati
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1750

Abstract

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.
Reliability-Aware Public Issue Priority Mapping for Indonesia’s Free Nutritious Meal Program Using Sentiment Calibration and BERTopic Rizaldi; Dewi Anggraeni; Abdul Kholiq
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1753

Abstract

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.
A Modular Agroclimatic Feature Engineering Framework for Country-Level Crop Yield Prediction Using XGBoost and LightGBM Ledyvia Audiz Coranov; Kusnawi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1762

Abstract

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.
Comparative Analysis of Similarity-Based Edge Construction Methods for Village Welfare Index Networks Abdullah Alhayad Arafah; Annisa; Sofyan Sjaf
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1769

Abstract

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.

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