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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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TELOS Feasibility Evaluation of the e-HakCipta System: Evidence from Intellectual Property Centers in Samarinda Reza Andrea; Sari Armiati; Tri Hannanto Saputra; Agustinus Fritz Wijaya; Aulia Khoirunnita; Suswanto; Hermawati
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.v8i3.1637

Abstract

This study evaluates the feasibility of the updated e-HakCipta system developed by the Directorate General of Intellectual Property (DJKI), Ministry of Law and Human Rights, based on user perceptions at Intellectual Property Centers (Sentra KI) in Samarinda. A descriptive quantitative approach was employed using a structured Likert-scale questionnaire validated by three domain experts. Data were collected from seven participating institutions, each represented by one designated e-HakCipta user. The evaluation applied the TELOS framework, covering Technological, Economic, Legal, Operational, and Schedule feasibility. The results show that all dimensions exceeded the feasibility threshold. Economic feasibility achieved the highest mean score (4.33), followed by Schedule (3.90), Technological (3.86), Operational (3.76), and Legal feasibility (3.67). The overall TELOS composite score of 3.90 indicates that the system is feasible. These findings suggest that the updated e-HakCipta system improves cost efficiency, registration speed, and interface usability, although legal compliance verification, plagiarism detection, and system stability require further enhancement.
Enterprise Architecture Design for the Electronic-Based Government System (SPBE) at the East Kalimantan Housing and Settlement Office Using the TOGAF ADM Framework Eko Junirianto; Agustinus Frizt WIjaya; Desi Pujiati; Dewi Safitriani; Reza Andrea
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.v8i3.1638

Abstract

The Electronic-Based Government System (SPBE) is a central component of Indonesia's digital governance strategy. At the East Kalimantan Housing and Settlement Office (Disperkim), the quality index of residential areas has persistently failed to reach its annual target, indicating fragmented information system use and structural inefficiencies in managing infrastructure, facilities, and public utilities (PSU) services. This study proposes an enterprise architecture design based on the TOGAF ADM framework to address this gap. Employing an architectural design research approach, the study collected primary data through semi-structured interviews with five Disperkim stakeholders and secondary data from Presidential Regulation No. 95 of 2018 on SPBE. The resulting blueprint covers four domains, business, data, application, and technology, and is aligned with organizational performance indicators and regulatory requirements. A key contribution is the proposed integration of the Sakti Gemas mobile application as a centralized public service platform that is expected to support more responsive PSU service delivery. The proposed architecture was validated at the design level through a Focused Group Discussion (FGD) with key Disperkim stakeholders, confirming its contextual appropriateness and technical feasibility. No implementation or quantitative service-performance evaluation has been conducted; operational impact can only be measured after system deployment. This study contributes a replicable architectural model for improving digital governance effectiveness in similar regional government institutions.
Spatio-Temporal Graph-Based Hotspot Analysis of Earthquake Events Using Spatial Autocorrelation and Community Detection in Indonesia Ika Arfiani; Herman Yuliansyah; Nur Rochmah Dyah Puji Astuti; Arfiani Nur Khusna
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.v8i3.1641

Abstract

Analysis of clustered seismic regions is important for understanding seismic activity patterns in tectonic regions such as Indonesia. However, conventional spatial statistical approaches generally analyze earthquake events independently and fail to capture complex spatio-temporal relationships. This study proposes a graph-based spatio-temporal hotspot analysis approach integrating spatial autocorrelation and community detection to identify regional seismic interaction patterns. The dataset used consists of 3,000 earthquake events from 2008–2025. Spatial autocorrelation was analyzed using Moran’s I, while earthquake relationships were modeled using a spatio-temporal graph with spatial and temporal thresholds of ≤400 km and ≤60 days. The results showed significant positive spatial autocorrelation with Moran’s I = 0.3367 (p = 0.001). The resulting graph consisted of 3,000 nodes and 22,896 edges, revealing substantial regional-scale connectivity and 14 major clusters with a modularity score of 0.7405, indicating a strong community structure. Degree centrality analysis identified highly connected nodes with a maximum degree of 77. These findings indicate that integrating spatial autocorrelation and graph analysis provides a more comprehensive representation of seismic interaction patterns and may support future seismic risk assessment in tectonically active regions.
Residual-Based Hybrid SARIMA–LSTM for Bali Tourism Demand Forecasting Using Google Trends Junaedi; Aditiya Hermawan; Yusuf Kurnia; Ardiane Rossi Kurniawan Maranto
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.v8i3.1644

Abstract

Accurate tourism demand forecasting is essential for destinations characterized by strong seasonality, nonlinear fluctuations, and post-pandemic recovery uncertainty. This study develops a residual-based hybrid SARIMA–LSTM model for forecasting monthly international tourist arrivals to Bali, Indonesia, using historical arrival data and Google Trends search query data. The dataset covers January 2009 to December 2024, comprising 192 monthly observations. A chronological split was applied, with January 2009 to December 2022 used for training and January 2023 to December 2024 used for testing. SARIMA was employed to capture linear and seasonal structures, while LSTM was used to learn nonlinear residual patterns. The proposed model was compared with SARIMA, Random Forest, standalone LSTM, and SARIMA–RF using RMSE, MAPE, and R². The SARIMA–LSTM model achieved the best performance, with RMSE = 35,915.36, MAPE = 5.64%, and R² = 0.68, compared with SARIMA, which obtained RMSE = 37,052.68, MAPE = 5.70%, and R² = 0.65. These findings indicate that residual-based hybridisation provides incremental forecasting improvement. However, the independent contribution of Google Trends is not separately isolated in this study and should therefore be interpreted cautiously as a complementary behavioural signal within the proposed forecasting framework.
Hypertension Classification Using Correlation-Based Feature Selection (CFS) with Random Forest, XGBoost, and Support Vector Machine: A Comparative Study on Indonesian Hospital Data Faradillah; Herri Setiawan; M Fadhiel Alie; Atthiyah Gisca Ahsya
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.v8i3.1646

Abstract

Hypertension is a major global health problem that significantly contributes to cardiovascular disease and mortality. This study evaluates the performance of Random Forest, XGBoost, and Support Vector Machine (SVM) algorithms integrated with Correlation-Based Feature Selection (CFS) for hypertension classification using hospital clinical data. The dataset comprises 500 clinical records containing demographic and physiological variables. CFS was applied to reduce irrelevant and redundant attributes before model training. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC through 10-fold cross-validation. Statistical significance was examined using the Friedman test followed by the Wilcoxon signed-rank test with Bonferroni correction. The results show that CFS improved classification performance across all models by approximately 5–6%. XGBoost achieved the best performance with 93.5% accuracy and 0.96 AUC, followed by Random Forest and SVM. However, systolic and diastolic blood pressure, which define the hypertension label, were retained as predictors, indicating a diagnostic classification design rather than independent risk prediction. Therefore, the findings should be interpreted as dataset-based hypertension classification, not future hypertension risk prediction.
ESRGAN-Enhanced YOLOv12 for Rice Leaf Disease Detection with Dataset Partitioning Analysis Ahmad Fathir; Ida Mulyadi; Fahrim Irhamna
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.v8i3.1648

Abstract

Rice leaf diseases pose a significant threat to agricultural productivity, yet accurate automated detection remains challenging due to low image quality in field conditions. This study proposes the integration of ESRGAN-based super-resolution with YOLOv12 for rice leaf disease detection using a dataset of 6,204 annotated field images spanning five classes: bacterial leaf blight, brown spot, healthy leaves, hispa, and leaf blast. To prevent data leakage, all images were partitioned into training, validation, and testing subsets prior to augmentation; under the S3 scenario (75:10:15), the training set was expanded from 4,653 to 13,959 images through augmentation. ESRGAN RRDB 4× enhancement was applied exclusively to all test-set images, enabling a clean before-and-after comparison without contaminating training data. The primary finding is that ESRGAN produces a modest but consistent improvement in detection performance: mAP@0.5 increased from 0.949 to 0.955, and mAP@0.5:0.95 increased from 0.910 to 0.925. Per-class analysis shows the largest gains in visually challenging classes, particularly Leaf Blast (+0.073) and Hispa (+0.060). Additionally, four dataset partitioning scenarios (S1–S4) were trained and evaluated under identical settings; as a preliminary observation, the S3 configuration offered a balanced trade-off between training data availability and test-set reliability, though definitive conclusions require further validation through repeated experimental runs.
A 1D-CNN Model with Modified MITDB-SVDB Dataset for Multiclass Arrhythmia Classification Muhamad Akbar; Muhammad Irvai
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.v8i3.1649

Abstract

Automated arrhythmia classification from electrocardiogram (ECG) signals remains challenging because public datasets are highly imbalanced and fine-grained multiclass performance may degrade when labels are mapped to the clinically standardized AAMI EC57 grouping scheme. This study proposes real-record dataset enrichment combined with a compact one-dimensional convolutional neural network (1D-CNN) for both fine-grained and AAMI-grouped beat classification. Fourteen records from the MIT-BIH Supraventricular Arrhythmia Database were inserted into the MIT-BIH Arrhythmia Database, adding 4,649 S beats, 4,530 V beats, and 47 Q beats without synthetic oversampling. Preprocessing included Christov R-peak segmentation, beat extraction, per-beat min-max normalization, and resampling to 180 Hz. The 1D-CNN was evaluated under 16-class, 17-class, and 5-class AAMI EC57 schemes. Using ASGD, the model achieved accuracies of 99.10%, 98.58%, and 99.38%, with macro F1-scores of 0.90, 0.87, and 0.97, respectively. Cross-database testing on INCARTDB reached 99.13% accuracy across four mappable classes (N, V, R, A), indicating limited 4-class transferability rather than full AAMI generalization. The approach preserves authentic ECG morphology while addressing minority-class scarcity. The findings show that real-beat enrichment can improve balanced ECG classification, although results are based on beat-level random splits and require future record-wise validation before clinical deployment.
Multi-Seed Robustness Benchmark of Lightweight YOLO Models for Young Crescent Moon Detection under Limited-Data Conditions Bayu Krisna Murti; Kartika Firdausy; Murinto
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.v8i3.1653

Abstract

Visual observation of the young crescent moon is challenging due to its thin and low-contrast appearance. Although YOLO-based object detectors are promising for image-based crescent localization, newer architectures do not automatically generalize well to small grayscale datasets, and prior studies rarely report robustness across repeated training runs. This study benchmarks YOLOv8n, YOLO11n, and YOLO26n for young crescent moon detection under limited-data conditions. A grayscale dataset of 697 images was resized to 640 × 640 pixels, annotated with the single class crescent_moon, and split into training, validation, and test subsets at a fixed 70:20:10 ratio. The three models were trained using the same configuration across five random seeds. Validation results were used to analyze multi-seed robustness, while the fixed 71-image test set was used for CPU-only inference evaluation. YOLO26n achieved the highest validation mAP@50-95 and fitness with the lowest variability, and also achieved the lowest CPU pipeline latency and highest throughput on the test set. These findings show that YOLO26n offers the best trade-off between accuracy and efficiency across the evaluated dataset and CPU-only inference setting. The reported throughput reflects low-frame-rate image-based inference, not real-time video performance. This study provides a reproducible benchmark protocol that combines fixed data splitting, grayscale preprocessing, data integrity checking, multi-seed robustness analysis, and CPU inference profiling.
Detection of SQL Injection, XSS, and Command Injection Attacks in Web Payloads Using SVM, Random Forest, and XGBoost Andrian Eko Widodo; Fabriyan Fandi Dwi Imaniawan
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.v8i3.1655

Abstract

Web application attacks, including SQL Injection (SQLi), Cross-Site Scripting (XSS), and Command Injection (CmdI), remain major threats to digital services. This study develops and evaluates an adversarial-aware protocol for multi-class malicious payload detection, focusing on accuracy, robustness against non-adaptive mutations, and practical inference feasibility. The protocol compares LinearSVC, Random Forest, and XGBoost with character-level neural baselines, namely character CNN and BiLSTM, and a transparent rule-based comparator. Evaluation integrates stratified sampling, deduplicated validation, mutation testing, SHAP-based interpretation, and end-to-end throughput measurement. Experiments used 49,998 stratified records from the SQLi-XSS-CommandInjection dataset in Google Colaboratory. On the internal test set, XGBoost obtained the best performance, achieving 99.28% accuracy and 99.32% macro F1-score. After removing 878 exact duplicate records for stricter re-evaluation, XGBoost maintained 99.21% accuracy and 99.24% macro F1-score, indicating that the findings were not driven solely by duplicate leakage. The complete preprocessing, feature extraction, and prediction pipeline reached an average CPU inference time of 0.832 ms per sample. SHAP analysis of Random Forest highlighted injection operators, script fragments, keyword hits, and structural tokens as discriminative features. The results provide a controlled benchmark, although validation on real HTTP logs remains future work.
Context-Aware Disaster Cause Mining from Indonesian Online News Using GA-Optimized Apriori: A Forest and Land Fire Case Study Qonitah Alia Puteri; Amalia Utamima
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.v8i3.1656

Abstract

Online disaster news contains reported cause information, but the narratives are unstructured and difficult to use for systematic disaster risk analysis. This study develops a context-aware disaster cause mining framework to extract and analyze reported cause-context association patterns from Indonesian online news. The framework integrates text-based cause extraction, contextual enrichment using population density and meteorological variables, GA-optimized Apriori-based Association Rule Mining, and merged rule interpretation. Disaster news records were transformed into transactions containing disaster type, reported causes, population density category, three-day rainfall category, and maximum temperature category. From 742 final transaction records, the Apriori process generated 200 initial association rules. After filtering rules with reported causes and contextual attributes in the antecedent and disaster type in the consequent, 97 target rules were retained. The empirical analysis focused on forest and land fire as a case study, producing 24 rules and 5 merged rule patterns. The strongest merged pattern was related to land burning, with a merged support of 0.1173. The findings show that the framework can organize disaster narratives into interpretable reported cause-context association patterns for disaster risk analytics. However, the results should not be interpreted as verified causal evidence.

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