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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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Concurrency Control in Distributed Databases: A Systematic Review Lloyd Moluma, Tshidiso; Esiefarienrhe, Bukohwo Michael
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5045

Abstract

This paper provides a comprehensive review of concurrency control techniques used in distributed database systems. It focuses on recent developments by examining articles and other academic documents published between 2016 and 2025. Using PRISMA 2020 guidelines, 197 scientific and academic studies were screened across major databases, and 10 articles met the final criteria for detailed analysis. The review classifies concurrency control approaches into four areas: types of locks, performance, accuracy, and efficiency. Each classification is then evaluated based on throughput, latency, detection accuracy, scalability, and technique applied to enhance these metrics. The findings demonstrate that traditional algorithms maintain consistent performance in general conditions but often struggle under heavy contention. Contrastingly, multi-version concurrency control and optimistic techniques improve scalability but introduce high abortion rates. Emerging adaptive techniques that depend on workload profiling show increasing promises for dynamic environments. The review highlights these trends and outlines future research direction for resilient distributed systems.
A Neural network model for the prediction of cattle prices in South African livestock actions Kgopa, Alfred
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5046

Abstract

Abstract – This study developed a neural network model for predicting cattle prices in South African livestock auctions based on breed (B), weight (W), time/season (T) and price (P) variables. Using the online auction dataset from 2023 - 2025, the model analyzed nonlinear relationships influencing price fluctuations, producing realistic per-cattle predictions ranging between ZAR 7,000 - ZAR 17,500, with projected increases up to ZAR 22,000 in future weeks. The results demonstrate the model’s capacity to capture market dynamics shaped by breed attributes, seasonal demand fluctuations, and animal mass. The results illustrate that artificial intelligence-led techniques, including neural networks, can substantially improve market prediction accuracy, enhance profitability, and inform strategic decisions in the livestock sector. Furthermore, this study provides a foundation for future research to expand predictive modelling beyond cattle, contributing to the development of a comprehensive livestock price prediction system that integrates multiple animal types under a unified intelligent forecasting framework.
Faktor-Faktor yang Mempengaruhi Literasi Digital Remaja dalam Game Online: Sebuah Systematic Literature Review Syanandi, Muhammad Destara; Hidayanto, Achmad Nizar
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5049

Abstract

The growing prominence of online games as spaces for intensive digital interaction has encouraged adolescents to engage more actively in gaming activities, while simultaneously increasing their exposure to various online opportunities and risks. This condition requires adolescents to possess adequate digital literacy skills, enabling them to navigate information, interact safely, and understand the social implications of their behaviour in virtual environments. This study aims to identify individual, social, and platform-level factors that influence adolescents’ digital literacy within the context of online games, and to explain how the relationships among these factors are discussed in prior empirical research. Using a Systematic Literature Review method based on PRISMA 2020, this study analyses 26 articles selected through a process of identification, screening, quality appraisal, and data extraction. The results demonstrate that adolescents’ digital literacy is influenced by individual competencies, such as digital skills, self-efficacy, critical thinking, and psychological conditions; social factors, including parental mediation, peer support, and socioeconomic conditions; and platform factors, including game feature design, interaction mechanisms, and characteristics of metaverse environments. The relationships among these factors are mutually reinforcing, in which digital literacy develops when a responsive social environment and safe platform design support individual competencies. This study highlights the importance of an ecological approach to understanding and enhancing adolescents’ digital literacy in online games. It encourages further research to explore more specific cultural contexts and game mechanics.
Design and Implementation of mHealth-Based Early Warning Systems for Heart Disease: A Scoping Review Ntwari, Richard; Wesonga, Bob; Vallence Ngabo Maniragaba; Engwau, Tonny; Kabarungi, Moreen
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5051

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, with sub-Saharan Africa including Uganda experiencing a growing burden due to limited access to early detection and specialist care. In response, mobile health (mHealth) technologies have emerged as promising tools to support early cardiac risk detection and intervention, especially in low-resource settings. Following Arksey and O’Malley’s scoping review framework with enhancements a systematic search was conducted across five databases from January 2000 to April 2025. Studies were screened and selected based on predefined inclusion criteria, and data were charted across design elements, outcomes, and implementation contexts. Thematic analysis was applied to synthesize findings.Twenty-seven studies met the inclusion criteria. mHealth-based EWS frequently incorporate wearable sensors, mobile apps, and AI-driven analytics for real-time monitoring and risk prediction. While user-centered design enhances acceptability, clinical efficacy evidence is mixed and scalability remains under-explored. AI/ML integration shows promise in improving prediction and personalization, but challenges persist around interoperability and health system integration.mHealth-based early warning systems hold significant potential to address cardiovascular care gaps in resource-limited settings. To maximize impact, future interventions should prioritize clinical validation, adaptive AI integration, and sustainable scale-up models tailored to local infrastructure and user needs. These insights are critical for guiding policymakers, developers, and researchers toward more effective digital health strategies for CVD prevention.
Analisis Kapabilitas Elastic Endpoint Security Berdasarkan Kerangka Cyber Kill Chain untuk Penguatan Pertahanan Endpoint Pemerintah Fatikho Kautsar
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5052

Abstract

Cyber threats targeting government institutions continue to escalate in sophistication and operational structure. The 2021 BSSN Cybersecurity Monitoring Report identified web defacement and data breaches as the most prevalent incidents across government entities. The 2024 cybersecurity landscape further reinforces this trend, recording 330,527,636 malicious traffic anomalies nationwide and highlighting ransomware, illegal access, and data breaches as the top incident categories. These developments underscore the persistent exploitation of endpoint weaknesses, emphasizing the need for defense strategies grounded in adversarial attack-chain understanding. This study evaluates the detection capabilities of Elastic Endpoint Security as an Endpoint Detection and Response (EDR) solution through the Cyber Kill Chain (CKC) framework to enhance endpoint defense within government environments. Two realistic attack scenarios were executed to assess detection performance across CKC phases. The findings indicate that Elastic EDR effectively disrupts critical stages, particularly delivery, exploitation, and installation, while providing protective responses aligned with modern defense requirements. This study highlights the viability of open EDR solutions as adaptive, cost-effective defensive foundations for public-sector cybersecurity.
Long-Context Transformer Models for Meeting Summarization: A Comparative Study of Full Fine-Tuning and Parameter-Efficient Tuning Winarko, Edi; Katarina Keishanti Joanne Kartakusuma
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5054

Abstract

The growing volume of virtual meetings has increased the need for effective long-document summarization systems that capture essential discussion points from lengthy transcripts. However, existing transformer-based models often struggle to handle long-context inputs and require substantial computational resources for fine-tuning. Moreover, prior work provides limited comparative analysis of full fine-tuning and parameter-efficient fine-tuning (PEFT) specifically for meeting summarization tasks. This study systematically evaluates three long-sequence Transformer architectures—LongT5, BigBird, and LED—on the MeetingBank dataset using both full fine-tuning and PEFT strategies. Models are assessed through ROUGE scores, BERTScore, parameter efficiency, and qualitative error analysis. Experimental results show that LongT5 with full fine-tuning achieves the best performance (ROUGE-1 = 0.675, BERTScore F1 = 0.921), outperforming BigBird as the next-best model by 31.6% in ROUGE-1. PEFT reduces trainable parameters by over 90% and remains competitive only for LongT5 (ROUGE-1 = 0.543, BERTScore F1 = 0.872), while BigBird and LED experience severe degradation, producing semantically weak and incoherent summaries despite low validation loss. These findings highlight that PEFT effectiveness is highly model-dependent and that validation loss alone is an unreliable indicator of generative quality. The study contributes a comprehensive benchmarking analysis and practical insights into optimizing long-document meeting summarization under computational constraints.
Digital Forensic Readiness to Mitigate Insider Threats in the SaaS Cloud Environment Shoderu, Gabriel; Stacey O. Baror; Sheunesu Makura; Abiodun Modupe
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5059

Abstract

Cloud environments are now central to organizational operations and hold sensitive information and essential business processes that depend on trust between users and the systems they access. Insider threats present a significant challenge in this setting because individuals with legitimate access understand how these environments operate and can misuse their privileges. Traditional insider threat mitigation approaches are mostly reactive. They often rely on delayed evidence collection and post-incident investigation, which results in incomplete records, late detection, and increased organizational harm. This highlights the need for proactive strategies that identify suspicious behavior early and support reliable forensic investigation. This study addresses the lack of a clear Digital Forensic Readiness framework that can manage insider threats in cloud environments. It introduces a readiness model that integrates forensic principles with intelligent behavioral analytics to detect, interpret, and preserve indicators of insider activity in Software as a Service environments. The research includes a detailed review of existing literature, identifies gaps in insider threat mitigation, and presents a practical scenario that illustrates how the framework supports investigation. In addition, the study proposes a structured approach for extracting and preparing data to improve anomaly detection and timely threat recognition. The framework aligns with ISO/IEC 27043 standards by promoting modularity, scalability, and evidential reliability. This work contributes a proactive and forensically sound approach to insider threat detection and establishes a foundation for future validation and adoption across organizations.
Unsupervised Clustering of Vietnamese Positive and Negative News Using PhoBERT and DBSCAN Dinh, Long; Le, An
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): 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.v14i6.5065

Abstract

The proliferation of digital media has made detecting and analyzing sentiment trends in Vietnamese news content increasingly important. This paper proposes an unsupervised learning approach for clustering Vietnamese news articles into positive and negative sentiment categories. The model combines headline and content features using PhoBERT, a Vietnamese-optimized language model, with DBSCAN clustering. Text is encoded using PhoBERT-base for headlines (768 dimensions) and PhoBERT-large for content (1024 dimensions), then concatenated and reduced to 64 dimensions via UMAP before clustering. KeyPhoBERT extracts representative keywords to enhance interpretability. Evaluated on 1,180 manually annotated articles from university social media with inter-annotator agreement of Cohen's kappa 0.83, the model achieves F1-score of 94.37%, with Adjusted Rand Index of 0.87 and Normalized Mutual Information of 0.81. Comparison with BERTopic baseline demonstrates the effectiveness of our approach for Vietnamese sentiment clustering without requiring labeled training data.
A Deep Reinforcement Learning for Adaptive Spectrum Access in Geo-Enabled Cognitive Radio Networks Ntuli, Elesa; Du Chunling
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.v15i1.4958

Abstract

The radio spectrum is getting more crowded as more people and devices connect wirelessly. Cognitive Radio Networks (CRNs), which can find and use empty spectrum, help solve this problem. One common way they do this is by using a Geo-Location Spectrum Database (GLSD) to check which parts of the spectrum are free. But these databases are not always updated in real time, so they sometimes miss chances to use the spectrum or cause interference. This study looks at a better way to handle this by using Deep Reinforcement Learning (DRL). The system we designed learns from the environment and makes smart decisions about when and where to use the spectrum. We used a Deep Q-Network (DQN) to test it in a simulated environment. Our results show that this new method improved spectrum use by 27%, reduced interference by 35%, and made decisions 22% faster than older methods that rely only on the database. The model reached about 91% accuracy in its decisions over many tests. This means that adding DRL to geo-location systems can make spectrum use more efficient, especially in busy areas or places with limited internet access. We suggest trying this setup in the real world using Software Defined Radios (SDRs) to see how it performs outside of simulations.
A Comparative Study of Generative Adversarial Networks (GANs) in Medical Image Processing: A Review Marwa M Abdulqadir; Adnan Mohsin Abdulazeez
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.v15i1.5047

Abstract

Generative Adversarial Networks (GANs) have come to be as powerful in medical image processing with massive benefits in image quality, fusion, classification and segmentation tasks. This paper displays an in-depth analysis of GAN structures and their use cases in medical image analysis for data augment, anomaly detection, cross- modality synthesis, super-resolution, and image reconstruction. As the demand for automated diagnostic systems has increased, GANs provide an efficient way to synthesize realistic medical images, particularly in domains with limited data availability. In this review, we discuss new developments and issues on how to apply GANs to accurate and effective medical image analysis. Moreover, this work explores the strengths, limitations, and comparative performance of different GAN models across diverse datasets and clinical tasks. By identifying key differences among the GAN models, and analysing performance, this review will be a roadmap for future studies in developing GAN-based models for better diagnosis and health applications.

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