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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.
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Articles 881 Documents
 83   84   85   86   87 
A Temporal Patient Risk Timeline Framework for Longitudinal Clinical Deterioration Surveillance Using Hospital Data Warehouse and Machine Learning Herwanto; Dian Nugraha; Popy Yuniar
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.v8i4.1680

Abstract

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 snapshot-based deterioration monitoring approaches.
Performance Trade-Off Analysis of Faster R-CNN with Grid-Based Histogram for Student Face Detection Arie Satia Dharma; Herimanto; Ranty Deviana Siahaan; Lamboy Albertson Sirait; Luna Sweeta Pangaribuan
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.v8i4.1681

Abstract

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.
Experimental Evaluation of Avalanche Effect and Hash Consistency in SHA-256, SHA3-256, and BLAKE2b-256 for Smart Grid Data Integrity Verification Muhammad Ridwan; Rusydi Umar; Muhammad Kunta Biddinika; Puguh Wahyu Prasetyo
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.v8i4.1685

Abstract

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.
Comparative Classification of Promotional Sources in Higher Education Admissions Using K-Nearest Neighbor and Naive Bayes Elly Yanuarti; Sujono; Djoko Soetarno; Rahmat Sulaiman
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.v8i4.1689

Abstract

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.
Reframing Technology Anxiety During Blended Learning Implementation: The Relationships among Perceived Safety, Social Influence, Satisfaction, and Behavioral Intention Harmonvikler Dumoharis Lumban Raja; Noryusliza Bin Abdullah; Deden Witarsyah; Rexon Nainggolan
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.v8i4.1690

Abstract

This study examines the relationships among perceived safety, social influence, technology anxiety, user satisfaction, and behavioral intention in blended learning implementation, investigating whether technology anxiety can be interpreted as productive vigilance rather than merely a barrier to technology adoption. Based on the Transactional Theory of Stress and Coping, this research explores how institutional safeguards and social expectations influence technology anxiety among teachers in high-stakes educational settings. A quantitative cross-sectional approach was applied using purposive sampling involving 205 high school teachers in North Sumatra, Indonesia. Data were analyzed using PLS-SEM to assess the structural model and mediation effects. The results indicate that Perceived Safety (β=0.526, t=8.17, p<0.001) and Social Influence (β=0.232, t=3.60, p<0.001) positively influence Technology Anxiety, explaining 48.0% of its variance. Technology Anxiety positively affects User Satisfaction (β=0.774, t=17.40, p<0.001), which subsequently influences Behavioral Intention (β=0.503, t=4.92, p<0.001). The direct effect of Technology Anxiety on Behavioral Intention is insignificant (β=-0.167, t=1.63, p=0.104), while the indirect effect through User Satisfaction is significant (βindirect=0.389, p<0.001). These findings highlight that technology anxiety may represent heightened attentiveness associated with satisfaction rather than resistance, requiring further validation through longitudinal and experimental studies.
A Hybrid ACO-BPNN-XGBoost Model for Monthly Rainfall Time-Series Forecasting Syaharuddin; Safaruddin; Vera Mandailina; Abdillah; Mahsup
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.v8i4.1693

Abstract

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.
An Explainable PCA-XGBoost Model for Predicting Bloodstream Infection in Hemodialysis Patients Rani Zulaikha; Budi Warsito; Aris Sugiharto
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.v8i4.1694

Abstract

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.
Application-Driven Parallel Differential Evolution: A Systematic Mapping Review of Scalable Optimization Applications Said Iskandar Al Idrus; Rudi Setiawan
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.v8i4.1695

Abstract

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.
Extending the Technology Acceptance Model with Perceived Immersive Learning for VR Usage in Higher Education Ajub Ajulian Zahra Macrina; Iwan Setiawan; Darjat
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.v8i4.1696

Abstract

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.
Explainable XGBoost-Based Prediction of Student Reading Interest Using Digital Learning and Reading Behavior Indicators Delpiah Wahyuningsih; Heki Aprianto; Lili Indah Sari; Wishnu Aribowo Probonegoro; Sri Rahayu; Serly Oktarina
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.v8i4.1698

Abstract

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.

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