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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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An Integrated XGBoost-SHAP Framework for XAI-based Analysis of Agroclimatic, Agronomic, and Operational Labor Proxy Affecting Block-Level Oil Palm Productivity Hafis Ramadhan; Ahmad Jazuli; Esti Wijayanti
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.v8i4.1770

Abstract

Oil palm block-level productivity (Yield Per Hectare/YPH) relies on complex agronomic, climatic, and operational interactions, yet existing predictive models often neglect internal estate conditions. We developed an XGBoost-SHAP framework using 1,351 block-year observations (2018–2024) from a single plantation in Central Kalimantan, Indonesia. Features include plant age, land area, rainfall, and a labor ratio functioning as a land-based workload proxy. The baseline random-split testing achieved R² = 0.6276, MAE = 2.2747 Tons/Ha, RMSE = 3.3303 Tons/Ha, and MAPE = 28.73%. However, evaluating the model under grouped block and time-based validation schemes revealed limited spatial and temporal transferability. SHAP analysis identified plant age (47.51%), land area (14.32%), annual rainfall (10.87%), and labor ratio (8.51%) as primary yield drivers, revealing non-linear interaction patterns. Rather than a fully autonomous tool, this framework provides a transparent interpretive heuristic and decision-support prototype to evaluate block-level operational strategies. By quantifying internal operational variables alongside agronomic factors, this study contributes valuable insights for precision agriculture, suggesting future work validate dashboard heuristics with estate managers and incorporate granular fertilization and harvesting logs to further enhance predictive robustness.
Sentiment Analysis of Public Opinion on The Plastic Waste Issue on Social Media X Using TF-IDF, Naïve Bayes, and SVM Ary Kania Sya'diah; Muhammad Arifin; Pratomo Setiaji
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.v8i4.1782

Abstract

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.
Optimizing Multiclass Android Malware Family Classification Using SMOTE-Tomek Links and XGBoost Ali Nur Ikhsan; Adam Prayogo Kuncoro; Debby Ummul Hidayah; Fajar Ramadhan
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.v8i4.1788

Abstract

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.
A Web-Based Spatial Decision Support System For Stunting Risk Prediction Using Random Forest and STBM Data Septia Oviyanti; Supriyono; Diana Laily Fithri
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.v8i4.1807

Abstract

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.
Integrating IndoBERT-Based Sentiment Analysis and PSI-TOPSIS Into a Decision Support System for Convection Production Prioritization Vitra Surya Ningrum; Anteng Widodo; Noor Latifah
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.v8i4.1809

Abstract

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.
Toward AI-Based Child Emotion Early Warning Systems in Child-Oriented Digital Environments: A Socio-Technical Systematic Review Ratih Titi Komala Sari; Widowati; Adi Wibowo
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.v8i4.1811

Abstract

AI-based emotion recognition is gaining increasing attention in child-oriented digital environments, including digital play, AI toys, online learning, therapeutic platforms, and interactive child-computer systems. However, the translation of recognized emotions into responsible caregiver-facing early warnings remains insufficiently explored. This systematic literature review synthesizes technical and socio-technical perspectives on child emotion recognition, focusing on sensing modalities, AI approaches, datasets, alert interpretation, trust, privacy, ethics, governance, and caregiver acceptance. Following PRISMA 2020 guidelines, 400 records from Scopus and IEEE Xplore were screened, resulting in 32 studies included in the core synthesis. The findings reveal that facial-expression analysis and convolutional neural networks dominate current research, while child-specific datasets, multimodal learning, real-world validation, uncertainty communication, privacy-by-design, and caregiver-centered evaluation remain limited. This review proposes a socio-technical framework linking AI emotion inference with explainable alert translation, privacy-aware governance, and caregiver decision support. The proposed emotion-to-alert mechanism remains conceptual and requires empirical validation before practical deployment.
Rule-Based Aspect Extraction and IndoBERT-Based Sentiment Classification of Ruparupa Mobile Application Reviews Erina Setyawati; Berlilana; Dhanar Intan Surya Saputra
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.v8i4.1816

Abstract

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.
An AI-Assisted Knowledge Management System Prototype for Fisheries Extension Officers: A Usability Evaluation Dani Saepuloh; Irman Hermadi; Yani Nurhadryani
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.v8i4.1817

Abstract

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.
Performance and Carbon Footprint Evaluation of a PWA-Based Waste Bank Information System Muhammad Dzulfiqar; Adzanil Rachmadhi Putra; Rosyid Abdillah
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.v8i4.1821

Abstract

The increasing volume of unmanaged waste in Indonesia has encouraged the adoption of digital waste bank management systems. However, the environmental impact of digital transformation, particularly regarding energy consumption and carbon emissions, remains insufficiently explored in community-scale information systems. This study evaluates the performance and environmental efficiency of Sibanksa, a Progressive Web Application (PWA)-based Waste Bank Information System, based on Green IT principles. A quantitative descriptive approach was applied by measuring four indicators: system performance, data transfer efficiency, estimated energy consumption, and digital carbon footprint under cached and non-cached loading scenarios. The results demonstrate that service worker caching significantly reduced data transfer size from an average of 4.84 MB to 20.9 kB, decreased estimated energy consumption from 0.0000594 kWh to 0.0000547 kWh, and reduced estimated carbon emissions from 0.044456 g CO₂ to 0.040886 g CO₂, representing approximately 8% improvements. Nevertheless, the overall page size remained relatively high (8.12 MB without caching and 7.46 MB with caching), while mobile rendering performance indicators, including First Contentful Paint (FCP) and Largest Contentful Paint (LCP), did not satisfy Web Vitals recommendations. This study contributes a practical measurement framework that integrates web performance analysis, energy efficiency assessment, and digital carbon footprint estimation for waste bank management systems. The findings provide empirical insights for developing more sustainable PWA-based information systems aligned with Green IT principles.
Machine Learning for Software Deployment in the Public Sector: A Systematic Review and Research Agenda for African Contexts Johnson Nuviadenu; Themba Masombuka; Ernest Mnkandla; Malusi Sibiya
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.v8i4.1839

Abstract

Failures in deploying digital public services can disrupt essential systems and affect millions of citizens. Machine learning (ML)-based software deployment decision support, including build risk prediction, release gating, autoscaling, rollback assistance, and post-deployment anomaly detection, offers opportunities for safer and more reliable releases. However, the extent of existing evidence in public-sector environments, particularly within African institutions, remains unclear. Following the PRISMA 2020 guidelines, this systematic literature review searched five databases using predefined inclusion criteria and a six-item quality assessment. A total of 33 peer-reviewed studies published between 2018 and 2025 were included, while studies focusing exclusively on MLOps were excluded. The findings reveal that none of the reviewed studies (0/33) explicitly evaluated ML deployment decision support in public-sector contexts or African institutions; existing evidence originates primarily from private-sector or unspecified environments. Research efforts are concentrated on autoscaling (12/33, 36%) and build prediction (9/33, 27%), with tree-based models being the dominant approach (16/33, 48%). Furthermore, only one study (3%) reported statistical significance testing or confidence intervals. This review identifies a research and evidence gap rather than confirming the absence of practical adoption. It proposes a staged research agenda toward explainable, lightweight, and context-aware ML deployment support for African public institutions.

Page 88 of 89 | Total Record : 881

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