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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 1,193 Documents
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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.
Pemanfaatan Jaringan Syaraf Tiruan untuk Pengaturan Kualitas Udara di Fasilitas Pengolahan Limbah dengan Teknologi Eco-Enzyme Rachmad Saptono; Mochammad Junus; Dwina Moentamaria; Setyaputra Pradana D.; Daffa Afrizal W.; Indra Lukmana P.
The Indonesian Journal of Computer Science Vol. 15 No. 1 (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.v15i1.5053

Abstract

This study presents the development and implementation of an intelligent air quality monitoring system for waste treatment facilities utilizing Artificial Neural Networks (ANN) and eco-enzyme technology to address hazardous gas emissions including hydrogen sulfide (H₂S), ammonia (NH₃), and methane (CH₄) in landfill areas. The system integrates MQ-series gas sensors (MQ-136, MQ-137, and MQ-4) with an ESP32 microcontroller for real-time data acquisition, achieving exceptional performance with prediction accuracy exceeding 90% and a final Mean Squared Error of 0.006 after 2000 training epochs. Correlation analysis revealed strong negative relationships between gaseous pollutants and the Quality Index (r = -0.96 to -0.97, p < 0.001), confirming these gases as reliable predictors of air quality degradation. Cloud integration through Google Firebase and Blynk platforms enables remote monitoring and real-time data accessibility, while the implementation of eco-enzyme technology demonstrated significant effectiveness in reducing harmful gas emissions and mitigating unpleasant odors, with measurable improvements observed during the observation period. The system's ability to provide accurate predictions, automated control mechanisms, and comprehensive real-time monitoring positions it as a valuable tool for pollution mitigation efforts in landfill environments, contributing positively to public health outcomes and environmental sustainability.
Hybridized Machine Learning based IDS for Anomaly Detection: A Systematic Review Victor Mathebula; Bukohwo Michael Esiefarienrhe
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5108

Abstract

Intrusion Detection Systems play a crucial role in safeguarding networks against increasingly sophisticated cyber threats. Traditional Intrusion Detection Systems approaches often struggle with adaptability and high false-positive rates. This review investigates the use of hybridized Machine Learning models for anomaly detection in IDS to enhance detection accuracy and system robustness. This study applies the PRISMA framework to analyze hybrid machine learning techniques applied to improve the performance of Intrusion Detection Systems, the datasets used, performance evaluation, identification of challenges, and knowledge gap analysis. Results show that hybrid ML models consistently outperform single-model approaches, achieving an accuracy of up to 99.99%. Despite promising results, challenges such as class imbalance and limited real-time deployment persist. From this systematic review, it is evident that hybridizing machine learning algorithms in Intrusion Detection Systems offers a powerful approach to anomaly detection, improving precision and accuracy.      
The Minimal isiNdebele Explicit and Implicit Question-answering Text Datasets for Zero-shot Learning Promise Malatji; Thipe Modipa
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5112

Abstract

The use of Large Language Models (LLMs) for task specific has gained more popularity for resourced languages like English due to availability of text data. There are no readily available datasets to fine tune for task specific in low resourced languages like isiNdebele. Data scarcity remains the main obstacle for many low resourced languages as it hinders on the development of language models. In this study we proposed the creation of two isiNdebele Question-answering (QA) text datasets, for explicit and implicit QA. Forty-five matric question papers from the South African Department of Basic Education website were downloaded. A data verification process was performed using human-in-the-loop technique to validate the created datasets. Further data augmentation was performed to increase the implicit dataset from 3012 to 36560 context-question-answer triplets. The augmented implicit dataset was then used to fine-tune the mT5 model on a zero-shot. The two datasets were accepted with a 98.33% acceptance percentage by the participants. The mT5 model performed exceptionally with ROUGE-L and BERTScore F1 of 0,992 and 0,999 respectively. The model made 94.7% accurate predictions with a perplexity score of 1,157. The results indicate that the multilingual model and transfer learning have great potential of dealing with low resourced languages in such as QA.
Misinformation Detection in Low-Resource Languages and Health Domains: Review and Evaluation Framework Bassey Isong; Rose Linah
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5129

Abstract

Misinformation on digital platforms harms public health decisions, electoral processes, and institutional trust. Its detection in low-resource languages (LRLs) remains structurally neglected. No prior survey applies a scoring framework to assess study quality or enable cross-study comparison. This review examines 51 peer-reviewed studies published between 2021 and 2026, following the PRISMA 2020 protocol across four dimensions: detection methodology, dataset coverage, health-domain adaptation, and explainable AI (XAI) integration, and proposes the LRL misinformation evaluation framework (LRLM-EF). The five-criterion evaluation framework was applied retrospectively to all reviewed studies. The findings reveal that dataset construction is the dominant research activity, with new corpora built for Amharic, Bangla, Bengali, Luganda, Sepedi, Sesotho, Xitsonga, isiZulu, Kurdish Sorani, and several Arabic dialects. Transformer-based models outperform classical and deep learning baselines in most settings; classical classifiers achieve comparable results where annotated data is scarce. Health-domain coverage is narrow. Research mostly concentrates on COVID-19 and vaccine misinformation, while HIV, malaria, and reproductive health appear in no reviewed studies. Multimodal fusion improves detection in all five studies where it was tested, yet audio-based detection in any LRL setting is absent. XAI is applied in a few studies, exclusively through post-hoc LIME, with no study evaluating its effect on user decisions. LRLM-EF scoring reveals that most studies address fewer than half the framework criteria, with adversarial evaluation and standardised reporting as the weakest dimensions. However, two contradictions exist in the evidence. Classical retrieval outperforms neural similarity on rare-terminology datasets, and augmentation volume shows no reliable accuracy gain, which further expose absence of a shared benchmarking standard.
Impact of 5G in Agricultural Networks: A Review of Improvement Strategies and Publication Trends Alfred Kgopa
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5130

Abstract

This study presents a systematic literature review of the impact of fifth-generation (5G) wireless networks on agriculture, focusing on publication trends, challenges, and opportunities from Scopus papers published between 2015 and 2025. The findings reveal a rapid annual growth rate of 34.93% in research outputs, with 274 documents published across 96 sources, indicating rising global interest in 5G-enabled smart farming technologies. However, persistent challenges include infrastructural limitations in rural farm areas, high costs of implementation, and digital literacy gaps among smallholder farmers. The bibliometric analysis highlights strong international collaboration (28.47%) and dominant contributions from India, China, and the USA. Emerging research trends focus on artificial intelligence (AI), edge computing, and hybrid connectivity models, signaling future directions for 5G-based agricultural innovations. Beyond mapping publication trends, this study synthesizes technical integration pathways and policy-relevant strategies for deploying resilient 5G-enabled agricultural networks in underdeveloped regions.
INDIKATOR DAN METODE PENGUKURAN KINERJA SMART CITY : TINJAUAN LITERATUR Ghefira Nur Fatima; Muhayat; Siti Alayda Azzahro
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5131

Abstract

This study examines the evolution of smart city performance measurement, focusing on the development of concepts, the types of indicators applied, the measurement methods used, and the challenges found in this field through a comprehensive review of previous studies. The research analyzes 25 journal articles indexed in Scopus and SINTA that discuss topics such as smart city indicators, composite indices, Internet of Things (IoT), network Quality of Service (QoS), resilience, cybersecurity, and governance. The data were analyzed using thematic-comparative analysis and configurational analysis to identify conceptual patterns and methodological differences across the studies. The findings indicate a shift from technology-centered measurement models toward more comprehensive frameworks that incorporate multiple dimensions, including environmental, economic, mobility, governance, social, and digital infrastructure aspects. Differences in data normalization, weighting, index aggregation, and IoT-based data collection influence the accuracy and interpretability of performance assessments. The study also highlights the need to integrate cybersecurity, system reliability, and stakeholder collaboration to develop more robust and context-appropriate smart city evaluation frameworks.
A Comparative Analysis of Bi-LSTM and XGBoost for Time-Series Classification in Power System Stability using SMOTE and Focal Loss Amal El Arid; Mahmoud Samad; Ghalia Nassreddine
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5134

Abstract

The rapid deployment of renewable energy sources has raised several issues regarding grid stability. Renewable energy sources differ from traditional energy generation due to their volatile nature and dependence on climatic factors. Therefore, accurate predictions of smart grids' stability are essential for efficient energy management. Current approaches do not incorporate dynamic aspects into consideration. The purpose of this paper is to overcome this limitation through the development of a time series-based framework for smart grid stability detection. A bidirectional long short-term memory model is created to capture both short-term and long-term relationships of the grid frequency and power signal. A hybrid method is proposed combining the Synthetic Minority Oversampling Technique and focal loss to improve results for imbalanced datasets. An extreme gradient boosting model is trained based on flattened temporal sequences and statistical feature descriptions. The experimental findings indicate that the suggested framework demonstrates high predictive performance, with XGBoost achieving the best accuracy, while BiLSTM is effective for capturing temporal patterns and maintaining high stability in classification recall.
An Extensive Analysis and Taxonomy of Explainable Artificial Intelligence for Malware Identification Dauan Aziz; Firas Amien; Raghad Yousif
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5143

Abstract

As malware continues to evolve in sophistication and scale, traditional detection methods struggle to keep pace, especially when facing obfuscated or zero-day threats. In response, Machine Learning (ML) and Deep Learning (DL) techniques have shown significant promise in enhancing malware detection through pattern recognition and anomaly classification. However, their increasing complexity has introduced major interpretability challenges, particularly in high-stakes cybersecurity contexts. This paper provides a comprehensive survey of eXplainable Artificial Intelligence (XAI) methods applied to malware detection across diverse computing platforms, including Windows PE files, PDF, Linux, and hardware-based systems. We propose a novel taxonomy that categorizes explainable malware detection approaches by model transparency, explanation technique (model-agnostic or model-specific), and deployment environment. We also discuss major trends, highlight underexplored domains, and outline future research directions aimed at enhancing real-time interpretability, adversarial robustness, and human-in-the-loop integration. This work aims to bridge the gap between high-performance malware detection models and actionable, transparent security decision-making.
Design and Performance Test of Squirrel Cage Hydrokinetic Turbine Yin Yin Aye
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5144

Abstract

This paper focuses on the design and performance test of squirrel cage hydrokinetic turbine. The design water flow velocity is 1.2 m/s and the designed water turbine has 3 blades. Turbine design is calculated by changing the aspect ratio. According to design calculation, maximum torque is found on aspect ratio 0.5. Squirrel cage hydrokinetic turbine is constructed by using the calculated design data. The turbine design diameter is 0.925m and height is 0.463 m. The turbine performance is tested at a canal near Mandalay Technological University. The experimental test results of rotational speed, angular velocity and turbine power for water velocity 1.2 m/s are 29 rpm, 3 rad/s and 68.2 W respectively. Moreover, theoretical and experimental test results of rotational speed, angular velocity and turbine power are compared by changing water velocity. According to the theoretical and experimental results, if the water velocity is increased, the rotational speed and turbine power are gradually increased. Keywords : aspect ratio, performance test, power, theoretical, water velocity

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