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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,170 Documents
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Computational Task Scheduling Across IoT-Edge-Fog-Cloud Continua: Algorithms, Adaptability and Research Gaps Isong, Bassey; Mamidza, Fulufhelo
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5104

Abstract

Internet of Things (IoT) deployments span heterogeneous infrastructure such as edge devices, fog nodes, and cloud servers, each with distinct computational capacity, energy constraints, and cost profiles. Scheduling across this three-tier stack requires satisfying four competing demands, including latency bounds, energy budgets, workload distribution, and cloud offloading cost. None of these can be optimized in isolation, and workload variability across deployment sites makes the problem even harder. In this paper, we review task scheduling strategies in edge-fog-cloud environments. It compares heuristic, metaheuristic, and machine learning-based approaches across deployment settings, adaptation capacity, and measured performance. Findings reveal metaheuristic methods reduce MK and energy consumption; learning-based approaches improve latency and task success rates, though under narrower conditions. Yet widespread reliance on simulation‑based evaluation and task-independence assumptions limits what these results actually demonstrate. Fixed objective weighting, unvalidated scalability, missing workflow dependency support, and static priority schemes each constrain deployment in practice. Future research should therefore prioritize shared or validated testbeds, workflow-aware/dependencies scheduling formulations, variable objective priorities, and scalability studies beyond small-to-medium topologies. Our study establishes a basis for designing scheduling strategies that hold under real deployment conditions across IoT, fog, and cloud applications and production settings.
A Digital Twin–Driven Machine Learning Framework for Diabetes Risk Prediction and Short-Term Health Trajectory Simulation Ndlovu, Belinda
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5110

Abstract

Diabetes remains a major global health challenge, requiring early risk detection and proactive management to reduce long-term complications. However, existing approaches are predominantly reactive and rely on static clinical indicators, limiting their ability to support personalized and forward-looking care. This study proposes an integrated framework that combines machine learning (ML) and digital twin (DT) technologies to enable both diabetes risk prediction and short-term health trajectory simulation. Using the CDC Diabetes Health Indicators dataset, a structured CRISP-DM methodology was applied to guide data preprocessing, feature selection, model development, and evaluation. Class imbalance (13.9% minority class) was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Five machine learning models were evaluated, with Gradient Boosting achieving the best performance (ROC-AUC = 0.797; F1-score = 0.415), indicating acceptable discriminative capability under imbalanced conditions. Building on this predictive layer, a digital twin framework was developed to simulate individual risk trajectories over a 90-day period. The system was operationalized through a web-based architecture that integrates prediction, simulation, and visualization into a unified interface. The results indicate that combining machine learning with digital twin modelling links point-in-time risk estimation with short-term trajectory exploration. While the simulation is based on model-driven assumptions rather than real-time physiological data, it provides an additional analytical layer that supports anticipatory decision-making. This study contributes a scalable, modular framework that bridges predictive analytics and simulation, offering a practical step towards more proactive, data-driven approaches in digital health.
Performance Analysis of Direct Stage, In-Direct Stage and Two-Stage Evaporative Air Cooler San, Thiri; Aung Ko Latt; War War Min Swe; Yin Yin Aye
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5114

Abstract

Evaporative coolers are used in various areas especially in dry, hot climates in weather. This research focus on the theoretical and experimental result of the evaporative air cooler. There are three types of evaporative air coolers. They are direct evaporative air cooler, indirect evaporative air cooler and two-stage evaporative air cooler. Evaporative air cooler can provide summer comfort conditions as an environmentally clean, fresh supply air and energy efficient cooling system for some regions of our country where direct system alone is not suitable. In this paper, the tube type heat exchanger was used ad the tubes are made with copper and water is passed through the copper tube with 20˚C. This model then has been adapted for two-stage, indirect and direct stages. Therefore, the cooling capacity of two-stage is 2.95 kW and direct stage and in-direct stage is 2.65 kW respectively. Therefore, the cooling efficiency of two-stage is 90% the direct stage is 84% and the in-direct stage is 80% of evaporative air cooler. So, two-stage evaporative air cooler was performed more than direct and in-direct stage evaporative air cooler.
A Hybrid Intelligent Model for Humanities Education: Integrating Multi-Agent Systems, Cognitive Computing, and NLP to Foster Independent Learning Mohamed Badawi Elkhalifa
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5116

Abstract

This study aims to design an intelligent e-learning system that integrates cognitive computing, multi-agent systems, expert systems, and natural language processing (NLP) techniques within a single unified platform. The system’s architecture is grounded in the independent learner model, which serves as the overarching pedagogical framework that organizes and regulates all its components. The researcher employed an experimental approach using a pretest-posttest design with two equivalent groups. The sample consisted of 50 male and female students from the College of Arts and Islamic Studies at Wasl University in Dubai. Participants were equally and randomly assigned to either an experimental group that received instruction through the proposed system or a control group that continued learning through conventional methods. Results of the independent-samples t-test revealed statistically significant differences at the (p < 0.001) level in favor of the experimental group across all three measured programming skills. The average effect size reached d = 2.71 according to Cohen’s coefficient, indicating a very large effect.
Comparative Topic Modelling of Mobile Banking User Reviews Using LDA and BERTopic: A Case Study of wondr by BNI Halim, Dicky; Budi, Indra; Mubina, Basma Fathan; Budi Santoso, Aris; Kresna Putra, Prabu
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5118

Abstract

This study explores user reviews of the Wondr mobile banking application to identify factors that influence user experience and service quality. The dataset, obtained from the Google Play Store, was processed through several preprocessing steps, including normalization, stopword removal, and stemming. Two topic modelling methods were applied: Latent Dirichlet Allocation (LDA) as a probabilistic baseline and BERTopic as an embedding-based approach. The LDA model was evaluated using coherence scores to determine the most suitable number of topics, while BERTopic was assessed based on topic distribution, interpretability, and additional coherence analysis. The results show that BERTopic produces more semantically meaningful and contextually rich topics, particularly in capturing short-text user reviews. Although BERTopic achieves lower overall coherence compared to LDA, certain topics demonstrate high semantic consistency, especially for well-defined issues such as login verification problems. The analysis reveals that most user feedback is concentrated on positive user experience, while critical issues related to login verification and system errors remain significant concerns. These findings provide actionable insights for improving mobile banking services and demonstrate the effectiveness of embedding-based topic modeling in financial text analytics. These findings highlight a trade-off between statistical consistency and semantic richness in topic modeling approaches. The results provide actionable insights for improving mobile banking services and demonstrate the effectiveness of combining probabilistic and embedding-based methods in financial text analytics.
Experimental and Performance Analysis of Axial Jet Fan with Diffuser Angle 5° Linn, Htet Htet; Swe, War War Min; Latt, Aung Ko; Kyaw, Sandar
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5119

Abstract

This study presents an experimental performance analysis of an axial jet fan with a converging nozzle and a 5° angle of diffuser, designed for basement car parking ventilation. The objective is to improve airflow quality and fan efficiency. The axial jet fan design parameters are computed with theoretical calculations and simulated computational fluid dynamics (CFD) the airflow characteristics and pressure distribution. The results were validated through experimental testing conducted in a fabricated tunnel. Parameters such as axial velocity, pressure distribution, and carbon monoxide (CO) concentration were measured at different fan speeds. The results indicate that the designed nozzle improves inlet airflow and diffuser pressure recovery, leading to improved fan efficiency. Additionally, CO concentration was significantly reduced over time, demonstrating effective ventilation performance. The study confirms that integrating nozzle–fan–diffuser components enhance operational efficiency of axial jet fan.
Contact Stress Analysis of A Spur Gear Using Lewis And Hertz Theory Oo, Than Zaw; Win, Htay Htay; Swe, War War Min; Aye, Yin Yin
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5121

Abstract

This paper focuses on the design and contact stress analysis of a spur gear for sugarcane juice extraction machine by changing gear face widths and three different materials (ASTM A 220-99, ASTM A 536-84 and C54400). Gear corrosion occurs at contact points as a result of bending stress and contact stress. This is the major source of the gear failure of the sugarcane juice extraction machine. This whole mechanism will be driven by a 1383W capacitor-start induction motor according to the motor power selection. Pitch diameters of 48 mm and module of 2.5 mm spur gear is selected in the design of gear. In theoretical analysis, the Lewis contact stress equation is used. In the numerical analysis, the geometry is created by SolidWorks software. The minimum von-Mises stress and effective strain are found that on ASTM A 220-99 material by using ANSYS 2020 R1 software. In this paper, von-Mises stress and effective strain are analyzed by changing the face widths of gears to 13 mm, 15 mm, 17 mm, 19 mm and 21 mm and using finite element analysis (FEA). Although all face widths are safe for this design, 17 mm is chosen in this paper due to power consumption and strength points of view.
Resource-Efficient Hybrid Ensemble ML Framework for Anomaly Detection in IoT Smart Homes Kgote, Otshepeng; Isong, Bassey
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5122

Abstract

The Internet of Things (IoT) technologies are used to support smart home systems through device and sensor connectivity for data exchange. However, the growth in adoption increases exposure to cyber-attacks and device faults, which puts system reliability and user safety at risk. This study proposes a framework that uses a pre-trained hybrid ensemble model to detect and separate attacks and faults while supporting timely mitigation. Firstly, the study evaluates models on the CICIoT2023 and IntelLab fault-injected datasets using ensemble learning methods and traditional supervised classifiers. Extreme Gradient Boosting shows the strongest intrusion detection performance. Random Forest shows the strongest fault detection performance. Secondly, both models were fine-tuned and combined within a hybrid meta-model. The results show high accuracy, strong F1 scores, and low false positive rates. The framework was implemented as a web application using Flask and Streamlit to support real-time simulations of attack, fault, and normal events. Evaluation reports latency under 5 seconds and memory use under 400 KB, which supports deployment on resource constrained IoT devices. It was optimized using quantisation and compression. The paper proposes a hybrid ensemble approach for joint fault and intrusion detection, a deployable prototype for constrained environments, and methods to enhance model performance.
An Integrated Machine Learning Framework for Early Prediction of Student Academic Performance in University Management Systems Mohammed, Omeed
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5125

Abstract

Forecasting student academic standing at an early stage is a critical challenge in higher education. This paper presents a supervised machine learning classification framework applied to final examination records from 1,015 students across the English and Translation Departments of Nawroz University, Duhok, Kurdistan Region, Iraq, spanning Semesters 1–6 under the Bologna Process. Six classifiers were evaluated using stratified five-fold cross-validation and an independent held-out test set. Each student’s six-course final marks served as input features; the output was one of four GPA-derived tiers (Excellent, Good, Satisfactory, Poor/At-Risk). SVM (RBF) achieved the strongest performance: 96.06% test accuracy, 94.09% cross-validated accuracy, and 92.55% macro F1-score. Results indicate that a lightweight ML pipeline using only routine assessment data can exceed 94% prediction accuracy, making it a viable early-warning component within existing university management system infrastructure.
GIS-Based Coverage Analysis of Public Bus Services in Yangon City, Myanmar Khin, May Thu Zar; Kyaw, Nyan Myint; Aye, Moe Thet Thet
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Public bus transport is the dominant travel mode in Yangon, serving millions of daily commuters and playing a vital role in urban mobility. This study presents a GIS-based coverage analysis of public bus services in Yangon City to evaluate spatial accessibility and identify service gaps within the transit network. Service effectiveness is assessed using coverage indicators, including service area buffers, population coverage ratios, and accessibility indices. The results show that central townships, such as South Okkalapa Township, Yankin Township, and Thaketa Township, achieve high coverage levels exceeding 80%, while peripheral areas, including Mingalardon Township and Dagon Myothit Township, remain below 40%. Heat map analysis highlights dense coverage in the central business district and surrounding areas, contrasted with limited accessibility in outer regions such as Hlaingtharyar Township. Key service gaps are also identified along major corridors like Parami Road and Bayint Naung Road. The study reveals significant spatial inequalities in transit accessibility and provides a data-driven basis for improving sustainable urban transport planning.

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