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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 761 Documents
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Real-Time Explainable Concept Drift Detection for Eco-Driving in Mining Trucks using KSWIN and Event-Triggered SHAP Kusnawi; Mochamad Agung Wibowo; Ridwan Sanjaya
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1551

Abstract

Fuel consumption represents a significant operational cost in mining, where real-time eco-driving optimization is hindered by dynamic and non-stationary operating conditions. Variations in operator behavior and environmental factors often induce concept drift, which diminishes the reliability of static machine learning models and constrains the effectiveness of conventional drift detection methods. This study proposes a distribution-aware, event-triggered Explainable Artificial Intelligence (XAI) framework for detecting and diagnosing fuel consumption anomalies in streaming telematics data. A Hoeffding Tree Regressor was evaluated using a prequential scheme on 1,927,867 real-world observations, achieving a Mean Absolute Error (MAE) of 19.43 under non-stationary conditions. Concept drift was monitored using the Kolmogorov–Smirnov Windowing (KSWIN) algorithm, which detected 1,874 drift events. Upon detection, an event-triggered SHAP module identified contributing factors, indicating that behavioral features such as engine speed and accelerator position were dominant contributors in early drift events. The primary contribution of this study is the integration of distribution-based drift detection with event-triggered explainability within a unified streaming framework, facilitating both anomaly detection and interpretable root-cause analysis.
Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation Belinda Ndlovu; Zvinodashe Revesai; Kudakwashe Maguraushe
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1553

Abstract

Electronic waste (e-waste) is the fastest-growing solid waste stream worldwide, yet formal collection systems remain limited. Many existing Internet of Things (IoT) solutions emphasize advanced functionality at the expense of cost efficiency and practical deployability. This paper presents a cost-optimized IoT architecture for real-time monitoring of e-waste bins. The proposed system adopts a four-layer architecture integrating ESP32 microcontrollers, ultrasonic sensors for fill-level detection, and infrared sensors for monitoring, supported by a Node.js backend that provides real-time data updates. System validation was conducted through sensor calibration (n = 30), functional testing, stress testing, and cost-performance benchmarking against RFID-, GSM-, and LoRa-based alternatives. Experimental results demonstrate a fill-level accuracy of ±3.2%, temperature precision of ±1.8°C, system reliability of 97.3%, uptime of 98.7%, and an average latency of 2.1 s. The deployment cost was USD 78 per bin, which is approximately 40% lower than comparable RFID-based systems. In addition, the system reduced unnecessary collection trips by 35% and yielded an estimated return on investment (ROI) of 8.5 months. These results show that a low-complexity, cost-efficient IoT design can provide a scalable and practical solution for e-waste bin monitoring.
ACTE: A Pilot Feasibility Evaluation of a Mastery-Aware Task Recommender for Mobile Language Learning in Real-World Contexts Yudhy Setyo Purwanto; Rahmat Gernowo; Dinar Mutiara Kusumo Nugraheni
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1554

Abstract

Mobile-assisted language learning (MALL) apps often present generic activities that ignore the semantic meaning of real-world places and provide limited skill-specific, mastery-based progression. This pilot feasibility study introduces the Adaptive Contextual Task Engine (ACTE), a lightweight on-device recommender that personalizes tasks using location semantics, CEFR-aligned modules, mastery status, and performance timing. ACTE was evaluated with 10 university students aged 18–23 in a simulated café environment to balance ecological validity and experimental control. Participants completed three A2 speaking tasks, the System Usability Scale (SUS), and a five-item relevance questionnaire. Results showed a mean SUS score of 72.0, exceeding the benchmark of 68. Participants rated task appropriateness for the café at 4.3/5 and real-life usability at 4.7/5, while 90% agreed that the tasks reflected authentic language use. Qualitative feedback confirmed contextual authenticity but indicated the need for clearer scoring explanations. These findings suggest that ACTE offers a practical, privacy-conscious, and replicable framework for situated MALL by linking semantic place affordances with mastery-based progression in controlled real-world simulations.
Security Analysis of Indonesian Region Government Web Applications Based on NIST SP 800-115 and WSTG v4.2 Arizal; Muhammad Hilal; Dimas Febriyan Priambodo
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1558

Abstract

The rapid adoption of e-government systems has increased the exposure of government web applications to cybersecurity threats with the lack of security-focused implementation. Previous studies on web application security assessment commonly using automated vulnerability scanners or validated with another tools, which may produce false positives and fail to provide comprehensive insights. This research addresses this limitation by conducting a structured and multi-target security assessment of regional government web applications. The assessment integrates a systematic penetration testing process with comprehensive web application security testing guidelines. Automated scanning using OWASP ZAP and Arachni was combined with manual validation to ensure the accuracy of findings. The results identified nine validated vulnerabilities in the government portal and public service applications, and ten vulnerabilities in the legal documentation system. A significant portion of initial findings were confirmed as false positives after manual verification, highlighting the limitations of automated tools. The most common vulnerabilities were related to security misconfigurations, including missing security headers, outdated JavaScript libraries, and insecure cookie settings that highlight on weak in configuration hygiene and dependency management in this regional goverment. This study also demonstrates that combining structured penetration testing with detailed validation provides a more accurate and reliable assessment of government web application security.
Immersive Technologies in 5G-Enabled Networks: A Systematic Review of Publication Trends and Adoption Recommendations Nkosikhona Theoren Msweli; Alfred Thaga Kgopa
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1559

Abstract

Fifth-generation (5G) infrastructure is unlocking the transformative potential of immersive technologies across various sectors. This research explores the nexus between immersive technologies and 5G network deployments, based on trends in publications, factors in adoption, as well as policy aspects. Adopting a systematic approach, peer-reviewed publications were analyzed, considering only publications from the previous 10 years, i.e., 2015-2025.  The PRISMA approach was used to identify research articles that resulted in 43 studies selected for the review. The analysis of reviewed literature shows a surge in publications after 2018, with healthcare, academia, and sectoral training being the predominant fields of application. The synthesis of existing studies further reveals that infrastructural barriers are the most frequently reported constraint to adoption, followed by technical limitations and device readiness challenges. These constraints are severe and endured in developing countries. The reviewed literature identifies approaches for the telecommunications industry and policymakers, such as selective 5G deployments across different organisations to enable experimentation. Notwithstanding 5G technological advancements, it is unclear how organisations would adapt to such advancements, a gap that makes it challenging to assess its wider sectoral contributions. There is minimal research focusing on 5G adoption barriers, a gap that points to a need for future research adopting different methodological approaches, to investigate opportunities alongside risks. This research contributes to the analysis by tracking trends and barriers, along with providing actionable avenues for scaling immersive applications in constrained-resource settings.
Reinforcement Learning–Guided Hyperparameter Tuning for U-Net-Based Super-Resolution of Brain MRI Under Synthetic Degradation Suci Ramadini; Julian Supardi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1565

Abstract

Low-resolution magnetic resonance imaging (MRI) may reduce visibility of fine anatomical details, motivating computational super-resolution (SR) to enhance perceived image quality. This study proposes an SR pipeline for 2D brain MRI images using a U‑Net baseline model and a reinforcement learning (RL) agent to automate hyperparameter tuning. Because the selected public dataset does not provide paired low-resolution/high-resolution (LR–HR) images, LR inputs are generated synthetically using a controlled degradation process (blur–downsample–upsample–noise), with deterministic degradation for validation and testing to ensure stable evaluation. The baseline U‑Net is trained using an L1 objective (optionally mixed with differentiable SSIM loss), AdamW optimizer, and ReduceLROnPlateau scheduler guided by validation PSNR. A Double Deep Q‑Network (Double DQN) agent then selects discrete action combinations of learning rate and SSIM-weighted loss mixing to fine-tune the baseline. For the held-out test set (n=60), the baseline improves degraded inputs from 27.04±3.21 dB to 30.10±3.59 dB PSNR and from 0.706±0.132 to 0.875±0.064 SSIM, respectively. RL fine-tuning yields a modest additional PSNR gain to 30.20±3.58 dB and SSIM remains comparable at 0.873±0.066. The paired statistical tests confirm that the PSNR improvement is significant (p<0.01), while changes in SSIM are not statistically significant, suggesting that for the tested synthetic degradation setting RL can provide reliable but incremental refinement when the baseline is already strong.
Ensemble Learning for Pediatric Stunting Detection: A Comparative Study of XGBoost, Random Forest, and LightGBM with Oversampling Techniques Tri Sugihartono; Djoko Soetarno; Rahmat Sulaiman; Sarwindah; Marini; Fitriyani
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1568

Abstract

Stunting, driven by chronic childhood malnutrition, remains a critical global public health concern. Early detection is persistently challenged by class imbalance in pediatric health datasets and the absence of systematic comparisons between oversampling strategies and ensemble classifiers. This study develops and evaluates an ensemble learning pipeline for stunting detection, benchmarking XGBoost, Random Forest, and LightGBM across five oversampling configurations — Original, SMOTE, ADASYN, Borderline-SMOTE, and SMOTE-ENN — using 10,000 pediatric health records from posyandu activities in Bangka Belitung Province, Indonesia. Seven anthropometric and demographic features were utilized, with stratified 80:20 train-test splitting and five-fold cross-validation. XGBoost with original imbalanced data achieved the highest Recall (0.9573) and a competitive F1-Score (0.9158), while LightGBM with SMOTE delivered the strongest balanced performance (F1-Score: 0.9160, ROC-AUC: 0.8431). SMOTE-ENN consistently underperformed across all classifiers. To our knowledge, this is the first study to simultaneously compare five oversampling strategies across three ensemble models within a unified framework, offering a foundation for high-sensitivity stunting surveillance in resource-constrained healthcare settings.
Comparative Performance Analysis of Dual-Prime RSA and Eight-Prime RSA Rahmat Sulaiman; Agustina Mardeka Raya; Djoko Soetarno; Tri Sugihartono; Ellya Helmud
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1569

Abstract

This study presents a comparative performance analysis of Dual-Prime RSA and Eight-Prime RSA by evaluating computational efficiency in key generation, encryption, and decryption at 1024-bit and 2048-bit key lengths. Experiments were conducted in a controlled environment, using processing time as the primary performance metric. The results show a consistent computational advantage for Dual-Prime RSA across all operations. At the 2048-bit key length, Eight-Prime RSA requires substantially more time for key generation, performing approximately 643% slower than Dual-Prime RSA, which highlights the overhead associated with increasing the number of prime factors. Decryption results further reinforce this gap: Eight-Prime RSA at 2048-bit records about a 247% increase in processing time compared with its own 1024-bit baseline and remains markedly slower than Dual-Prime RSA at the same key length. Although differences in encryption time are less significant, Eight-Prime RSA offers no meaningful efficiency advantage. While earlier studies suggest that additional prime factors may provide theoretical security benefits, this work is limited to empirical performance benchmarking and does not include a full security analysis. Overall, the findings indicate that Dual-Prime RSA is the more practical and scalable choice for real-world 2048-bit applications and performance-sensitive deployments.
Detecting Deceptive Online Reviews Using a Semantic Reliability Index and Hybrid Text Representation Hartatik; Andri Syafrianto
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1576

Abstract

Online review platforms such as Yelp play an important role in consumer decision-making, but the growing prevalence of fake reviews undermines their reliability. This study proposes a hybrid approach for fake review detection by integrating stylometric features, language model signals, and semantic embeddings within a unified classification framework. The proposed method combines linguistic indicators, including GPT-2 perplexity, lexical diversity, sentence burstiness, punctuation ratio, and sentiment intensity, with TF-IDF representations and Sentence-BERT embeddings. A composite feature, namely the Semantic Reliability Index (SRI), is introduced to capture interactions between semantic similarity and linguistic characteristics, serving as an auxiliary feature within the hybrid model rather than a standalone classifier. Experiments on a Yelp hotel review dataset demonstrate that the hybrid model outperforms baseline methods in terms of F1-score and AUC, indicating improved discriminative capability. It should be noted that the classification setting is based on a binary transformation of ordinal labels, which may simplify the underlying label structure and influence performance interpretation. Overall, this work's contribution lies in a systematic feature-integration strategy that enhances fake review detection in the evaluated dataset.
Enhancing YOLOv12-Based Rice Leaf Disease Detection through Evaluation of Three Data-Split Scenarios Ida Mulyadi; Fahrim Irhamna; Chyquitha Danuputri; Ridwang; Ridha Awalia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1580

Abstract

One of the most significant staple crops in the world is rice, and one of the main causes of the drop in agricultural yields is illnesses that affect rice leaves. To avoid large agricultural losses, early diagnosis of these illnesses is essential. The goal of this project is to use YOLOv12, the most recent deep learning-based object detection architecture, to create a rice leaf disease detection system. The model was trained using a dataset of 4,744 photos of rice leaves that included three disease classes: Leaf Blast, Brown Spot, and Bacterial Leaf Blight. Methods to boost variability and enhance detection performance, image preprocessing with data augmentation was used. Standard object detection criteria, such as mean Average Precision (mAP), precision, and recall, were used to assess the model. The YOLOv12 model was highly effective in detecting rice leaf illnesses. According to the experimental data, it achieved a mAP of 97%, a precision of 96%, and a recall of 96.5%. The use of YOLOv12's greater efficiency and quality in detecting small objects—which is essential for identifying illness symptoms on leaves—is what makes this study successful. These results lay the groundwork for upcoming precision agricultural real-time monitoring applications.

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