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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 832 Documents
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Enhancing Javanese Emotion Classification: A Comparative Study of Cross-Lingual, Supervised, and Hybrid Transfer Learning using IndoBERTweet Galih Setiawan Nurohim; Heribertus Ary Setyadi; Sigit Wahyudi; Paulus Tofan Rapiyanta
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1657

Abstract

This research investigates transfer learning efficacy for five-class emotion classification in Javanese Ngoko. A parallel Indonesian–Javanese Ngoko corpus was synthesized by translating 5,400 samples from the PRDECT-ID dataset using machine translation, with translation quality verified via a preliminary expert validation sample. Using IndoBERTweet as the backbone architecture, three paradigms were evaluated: zero-shot transfer (E1), fully supervised learning (E2), and cross-lingual transfer learning (E3) with identical hyperparameters. Empirical results indicate that the cross-lingual transfer (E3) setup achieved peak performance (67,5% accuracy; 0,67 weighted F1) under the evaluated dataset and experimental setting. Per-class analysis identified that positive affect (Happy) showed cross-lingual stability, whereas negative emotions (Sadness, Fear) suffered degradation due to lexical divergence between the two languages. Training dynamics revealed early-onset overfitting, suggesting model capacity exceeds current dataset density. This work establishes a baseline benchmark for Javanese emotion classification and provides a reproducible machine-translated parallel corpus, emphasizing the need for future validation with native-speaker data to mitigate translation bias.
A Robustness-Oriented Evaluation of LSTM, GRU, and Hybrid LSTM-GRU Models for ANTM.JK Stock Price Forecasting Khoirudin; Prind Triajeng Pungkasanti; Nur Wakhidah; Vinay Rishiwal
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1660

Abstract

Accurately forecasting stock prices remains challenging because of the nonlinear and volatile nature of financial markets, particularly during periods of heightened uncertainty, such as the COVID-19 pandemic. This study evaluates the robustness of three models, LSTM, GRU, and Hybrid LSTM-GRU, for ANTM.JK stock price forecasting using a volatility-oriented evaluation framework. Historical stock data from September 2005 to May 2022 were transformed into supervised time-series datasets using a 15-lag sliding window. The model performance was evaluated using baseline prediction accuracy, 5-fold chronological cross-validation consistency, and synthetic stress scenarios consisting of controlled price drops, price rises, and high-volatility noise. Evaluation metrics included RMSE, MSE, MAE, R, and R^2. The GRU model delivered the top baseline prediction results, achieving the smallest RMSE of 52.95 and MAE of 28.14. In cross-validation, the LSTM model recorded the lowest average RMSE of 119.41. Meanwhile, the Hybrid LSTM-GRU exhibited the highest prediction consistency and robustness across various synthetic stress scenarios. In contrast to earlier research that mainly focused on prediction precision, this study presents a comprehensive framework for evaluating robustness. This framework combines baseline accuracy, consistency through cross-validation, and an analysis of synthetic stress scenarios. The generated robustness map offers a systematic interpretation of model strengths across diverse evaluation goals, facilitating a more thorough assessment of stock-forecasting models in different market environments.
Evaluating Rural E-Government Web Service Using Design Thinking and COBIT 2019: A Case Study of Indramayu Regency Widya Cholil; Tri Rahayu; Tjahjanto; Prihandoko; Vidyasagar Potdar
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1661

Abstract

This study evaluates the maturity of public web services in Indramayu Regency, Indonesia, using a Design Thinking methodology combined with the COBIT 2019 Process Performance Model, aiming to clarify the digital governance gap in rural public service delivery. The research applies the five-stage Design Thinking process (Empathize, Define, Ideate, Prototype, Test) as an evaluation lens. Data were collected from 45 respondents through interviews, questionnaires, direct observations, and workshops with village officials. The COBIT 2019 Process Attribute Achievement Model was used to quantify capability maturity levels across each Design Thinking phase for 14 public web services in the regency. Most public web services scored only ‘Manage’ in the Empathize phase, with BPS Indramayu and PPID reaching ‘Established’. The Ideate, Prototype, and Test phases were predominantly ‘Performed’, with only BPS Indramayu and SIPADU reaching 'Managed'. These findings indicate that most platforms function primarily as informational websites with limited user-driven innovation, prototyping, and structured testing. This study offers a novel integrative framework that combines human-centered Design Thinking with COBIT 2019 governance assessment to assess the public sector web services of digital transformation in decentralized rural governance contexts, providing a scalable, empirically grounded model applicable to other rural communities.
Predicting Student Performance to Support Adaptive Content Delivery: A Random Forest Approach I Kadek Dwi Nuryana; Lintang Iqhtiar Dwi Mawarni
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1663

Abstract

This study addresses the prediction-to-action gap in student performance analytics by proposing an interpretable framework that transforms predictive risk scores into adaptive content recommendations. Rather than only identifying at-risk students, the framework integrates performance prediction, interpretable rule extraction, and decision-support simulation to guide adaptive learning interventions. The study used the Open University Learning Analytics Dataset (OULAD), comprising 6,937 student records after filtering and preprocessing from the original 32,593 records. A Random Forest-based framework was adopted because of its interpretability and rule-extraction capability, although XGBoost achieved slightly higher predictive performance. The framework consists of three components: student performance prediction, interpretable decision rule extraction, and a decision-engine simulation for adaptive content recommendation. The predictive model achieved 87.22% accuracy and an AUC-ROC of 0.932. Rule extraction generated 20 human-readable rules with an average of 2.0 conditions per rule, an interpretability score of 1.000, and 81.6% fidelity to the full Random Forest model. The decision-engine simulation classified students by risk level and produced corresponding adaptive recommendations. An estimated Adaptation Gain metric indicated a potential 53.54% improvement in projected student success rates under conservative simulation assumptions. The proposed framework connects prediction with actionable recommendations to support educational decision-making, although real-world intervention validation remains necessary.
Class-Level Behavior Analysis under Metric Disagreement in Imbalanced Multi-Label Indonesian Emotion Classification Jahda Rusti Putri; Ermatita; Abdiansah
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1664

Abstract

This study aims to analyze class-level model behavior under metric disagreement in imbalanced multi-label Indonesian emotion classification, using the divergence between Macro F1 and Micro F1 as a diagnostic signal rather than a mere performance indicator. A machine-translated Indonesian version of the GoEmotions dataset, comprising approximately 58,000 samples across 28 fine-grained emotion categories, is used as the experimental setting. The translated dataset was not manually revalidated, and findings are scoped to this translated GoEmotions setting. Two transformer-based models are evaluated: IndoBERT, a monolingual Indonesian model, and DistilBERT, a multilingual model, both fine-tuned with class-specific threshold optimization. The results reveal opposing divergence patterns: IndoBERT achieves higher Micro F1 than Macro F1, indicating performance concentrated on high-frequency classes, while DistilBERT exhibits the reverse pattern, suggesting broader but less precise label activation. Per-class analysis further shows that most minority classes consistently fall into unstable or non-functional performance regimes across both models. This study concludes that aggregate metrics alone are insufficient for evaluating model behavior in imbalanced multi-label settings. A behavior-oriented interpretation framework for Macro–Micro F1 divergence and a regime-based class reliability categorization are proposed to support more structured and informative evaluation practices.
Development and Evaluation of a Multi-Algorithm Application for Predicting Breast Cancer Patient Survival Zulkifli; Kraugusteeliana; Sukarni; Ikna Awaliyani; Nur Asini; Fitriana
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1665

Abstract

This study developed a multi-algorithm machine learning prototype for multiclass breast cancer survival prediction using 1,980 patient records, classifying patients as Living, Died of Disease, or Died of Other Causes. The framework integrated NN, SVM, RF, NB, and KNN algorithms within a decision-support monitoring application, with preprocessing steps including data cleaning, normalization, feature preparation, and dataset partitioning. To prevent target leakage, survival-related variables were excluded from the predictor set. The revised evaluation results indicated that NB and KNN delivered the strongest performance, achieving weighted average F1-scores of 0.93 and 0.92, respectively, while NN and RF showed comparatively lower results. These findings highlight the potential of machine learning for breast cancer survival status monitoring, although the proposed system is designed as a decision-support prototype rather than a clinical diagnostic tool. Therefore, before actual healthcare deployment, more research incorporating explainable AI techniques, external validation, and real-world clinical testing is needed.
Transfer Performance and LIME Explanation of Ensemble Classifiers in Cross-Project Defect Prediction Bassey Isong
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1667

Abstract

Ensemble methods are widely used in cross-project defect prediction (CPDP), particularly in projects that lack sufficient historical data by training on external source projects. However, no prior study has compared Bagging, Boosting, and Stacking directly under a Leave-One-Project-Out (LOPO) protocol or examined whether within-project performance rankings carry over to the cross-project setting. We evaluated three ensemble classifiers on five NASA MDP datasets sharing a common Halstead and McCabe feature schema. SMOTE is applied exclusively to pooled source data to prevent leakage into the target. A no-SMOTE baseline isolates the contribution of source-only SMOTE. LIME explanations are aggregated over thirty instances per model to assess feature importance consistency across the project boundary. Within-project evaluation shows Stacking achieves the highest F1 on four of five datasets, peaking at 0.503 on KC1. Under LOPO, these rankings reverse as Bagging and Boosting transfer more reliably, while Stacking's F1 drops by up to 0.258 points. Source-only SMOTE consistently improves transfer across all targets and ensembles. LIME consistency analysis produces undefined Spearman rank correlations, indicating that thirty-instance aggregation is insufficient to produce stable rank vectors for 21-feature datasets. To the best of our knowledge, this is the first study to compare all three ensemble strategies under LOPO on a shared NASA dataset feature schema. Particularly with a no-SMOTE control, aggregated LIME analysis, and a pilot meta-feature study identifying dataset size as the most actionable label-free predictor of ensemble suitability for CPDP deployment.
Comparative Evaluation of BiLSTM-CNN, XGBoost, and Ridge Regression for Heart Disease Classification on the Cleveland Dataset Ajimah Nnabueze Edmund; Ikiomoye Douglas Emmanuel; Esenogho Ebenezer
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1668

Abstract

Transformers have become the dominant architecture for tabular data modelling in natural language processing; however, their effectiveness for numerical tabular classification on modest sized and moderately imbalanced datasets remains unclear. This study evaluates the performance of hybrid deep learning and classical machine learning models which use the Cleveland Heart Disease dataset with 297 complete observations and was artificially constructed from 13 clinical features. The models examined include BiLSTM-CNN, Random Forest, XGBoost, Logistic Regression, and Ridge Regression. An experimental comparative approach was adopted under identical preprocessing, training conditions, and evaluation metrics, including accuracy, recall, F1-score, and Area Under the Curve (AUC). Results show that BiLSTM-CNN achieved the highest recall (0.8478), demonstrating strong minority class detection capability. Random Forest and XGBoost produced the best-balanced performance with 81.67% accuracy and the BiLSTM-CNN has the best F1-score of 0.8364, while Ridge Regression achieved the highest AUC (0.8945). This study provides empirical evidence that hybrid recurrent and ensemble models perform optimally on a small to medium sized Cleveland Heart Desease numerical tabular datasets without pre-training, offering practical guidance for Cleveland Heart Disease tabular clinical classification tasks, and no external validation was performed.
ROI-Based Shape-Prior Reconstruction for YOLOv8n-seg-Based Fetal Cerebellum Ultrasound Segmentation Yadi Utama; Erwin; Samsuryadi
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1669

Abstract

Fetal cerebellum segmentation in ultrasound images is important for quantitative analysis of fetal brain development, yet it remains challenging due to speckle noise, low contrast, acoustic artifacts, and unstable anatomical boundaries. This study proposes an ROI-Based Shape-Prior Reconstruction method as a post-processing refinement stage for YOLOv8n-seg fetal cerebellum segmentation. A total of 294 fetal ultrasound images with manually annotated binary cerebellum masks were used and divided into training, validation, and testing subsets using a 70:20:10 ratio. YOLOv8n-seg generated the initial segmentation masks, while the proposed ROI-based reconstruction stage refined the foreground region using a convolutional autoencoder trained on ROI-based binary cerebellum masks. Compared with raw YOLOv8n-seg, the proposed method improved DSC from 0.9282 to 0.9302 and IoU from 0.8671 to 0.8708. Boundary performance also improved, with HD95 decreasing from 15.06 to 14.18 and ASSD decreasing from 5.38 to 5.20. Although these improvements were modest and not statistically significant, the proposed method produced smoother boundaries and more morphologically consistent segmentation outputs in the visual evaluation. These results indicate that ROI-based shape-prior reconstruction can serve as a lightweight refinement stage for improving boundary consistency in fetal cerebellum ultrasound segmentation. However, external validation with larger datasets is still required to assess generalization.
A Hybrid LSTM-GNN-Q-Learning Model for Zero-Day Attack Detection: Evaluation on CICIDS2017 with Simulated Zero-Day Setting Musa L. Kazimoto; Juma S. Ally; Stanley Leonard
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1670

Abstract

Zero-day attacks exploit previously unseen vulnerabilities, making them difficult to identify using signature-based approaches. Their ability to bypass conventional detection mechanisms can result in significant financial losses, system compromise, and data breaches. To address this challenge, this study proposes a Hybrid Predictive Deep Learning (HPDL) model that integrates the Long Short-Term Memory (LSTM) network for modelling temporal relationships, Graph Neural Networks (GNN) for structural relationship modelling, and Q-Learning for feature weighting and adaptive decision making. The model was evaluated on CICIDS2017 dataset under a simulated zero-day setting by holding out four attack types (Brute Force, SQL Injection, XSS, and Infiltration), totaling 2,179 zero-day samples deliberately excluded from training and validation and used only for final testing. Experimental results show that the proposed HPDL model achieved a zero-day attack detection accuracy of 99.63% and F1-score of 0.9970, outperforming LSTM-only and GNN-only baseline models, which achieved accuracies of 98.5% and 85.0%, respectively. These results indicate that integrating temporal, structural, and reinforcement learning paradigms provides an effective approach for zero-day attack detection.

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