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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 1,114 Documents
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Mitigation of Denial of Service Attacks in Software-Defined-Cognitive Radio Networks Using Software-Defined-Cognitive Radio Shield Mampuele, Lebepe; Velempini, Mthulisi
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4951

Abstract

A user-friendly approach to managing network resources which solve a number of management-related concerns is provided by software-defined networks. While on the other hand, a novel paradigm called Cognitive Radio Networks (CRN) was developed to address spectrum scarcity by employing dynamic spectrum access. CRN enables secondary unlicensed users to utilize the licensed spectrum when idle without interfering with authorized users. Unfortunately, the two network technologies are susceptible to security attacks. Network security planning as the first step in network protection is therefore fundamental. The techniques employed by software-defined cognitive radio networks to detect and counteract denial of service (DoS) attacks are presented in this study. We then design an intrusion detection system (IDS) to address the effects of DoS attacks. In this approach, the IDS is connected to the software-defined cognitive radio network's controller. We considered the detection time or the amount of time it takes to detect an attack, the payload, the jitter, and the packet drop rate, as evaluation metrics. The round trip time and throughput were also considered. To generate the findings and compare them to those of existing schemes we used NetSim, which was installed on the Windows 10 Operating System. The simulation results show that the proposed scheme is efficient.
Enhanced Detection of IoT-Based DoS Attacks Using A Hybrid ANN-RF Classification Model Ndaba, Solomon Bulelani; Mathonsi, Topside E.; Plessis, Deon Du
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4965

Abstract

Denial of Service (DoS) attacks pose a significant threat to the integrity and availability of Internet of Things (IoT) networks, where interconnected devices are increasingly targeted due to their vulnerabilities. These attacks overwhelm systems with excessive traffic, disrupting legitimate services and potentially compromising sensitive data. Traditional detection methods often rely on predefined signatures, which struggle to keep pace with the evolving tactics employed by attackers. This study introduces a novel hybrid detection algorithm that integrates Artificial Neural Networks (ANN) and Random Forest (RF) classifiers, termed ANN-RF, to enhance the detection of DoS attacks in IoT environments. The ANN-RF model was evaluated based on critical performance metrics, including detection accuracy, False Positive Rate (FPR), and latency. Experimental results obtained through MATLAB demonstrate that the ANN-RF model achieves a detection accuracy of 93% and a low FPR of 5% when detecting 30 attacks, significantly outperforming standalone ANN and RF models, which recorded accuracies of 82% and 87%, and FPRs of 15% and 10%, respectively. Additionally, the ANN-RF model consistently maintains high detection accuracy, reducing false alarms and enhancing reliability as the number of attacks increases. Thus, the proposed ANN-RF model has strong potential to enhance real-time security in IoT networks by offering a scalable, accurate, and adaptive solution for DoS attack detection, with practical applications across domains such as smart homes, healthcare, and industrial control systems.
A Review of Vulnerability Detection Algorithms in Software Code Zelda P. Ramahlo; Mathonsi, Topside; Tshimangadzo M. Tshilongamulenzhe
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4972

Abstract

Detecting software vulnerabilities is essential to keeping modern systems safe in the face of increasingly sophisticated cyber threats. This paper offers a clear and accessible overview of how vulnerabilities are currently identified, reviewing traditional, machine learning (ML), and hybrid approaches. Traditional techniques such as static and dynamic analysis are still widely used but often suffer from high false positive rates and struggle to adapt to new and evolving threats. In contrast, recent ML developments, especially those involving Random Forest (RF) and Convolutional Neural Networks (CNN), have shown significant promise in improving detection accuracy, feature extraction, and classification. Decision Tree methods remain valued for their transparency, while CNNs and other deep learning tools excel at recognizing structural and spatial patterns in code. Combining these strengths in hybrid models integrating effective feature selection with powerful pattern recognition has the potential to deliver more accurate results and reduce false alarms. However, persistent challenges remain, including limited dataset diversity, weak resilience against adversarial attacks, and the need for real-time adaptability. By bringing together the latest research and practical insights, this review aims to guide developers, security analysts, and organizations in creating more robust, automated, and adaptive security tools capable of meeting the fast-changing demands of software vulnerability management.
Study on the Surface Water Allocation for Domestic, Agricultural and Hydropower for Myogyi Dam in Myanmar Thida Mon, Wint; May Ei Nandar Soe
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4977

Abstract

This study presents surface water allocation for domestic, agricultural, and hydropower sectors in the Kyaukse District of Myanmar, focusing on the Myogyi Dam as the primary water source. Myogyi Dam is constructed across the flow of Zawgyi river and divided into four canals such as Ngabyaung Canal, Thindwe Canal, Minye Canal and Zeedaw Canal. As domestic water distribution network, Zawgyi river is connected to Kyaukse township. Utilizing the CROPWAT 8.0 and WEAP models, the research analyzes water demand, unmet demand, and site coverage from the year 2024 to the year 2054. Crop water requirements are estimated using long-term meteorological data and local crop parameters and WEAP simulations evaluate allocation efficiency across interconnected demand sites. According to the results of reference scenarios, the domestic supply zone and most irrigated areas are able to meet their respective water demands. However, the Ngabyaung irrigated area face a significant shortfall, requiring an additional 6900 million gallons to fulfill its demand. While all other areas achieve full demand site coverage, Ngabyaung lag behind with only 25% coverage. The total unmet demand of hydropower is approximately 35,000 megawatt-hours, and the demand site coverage remains at 100% throughout the year, except during the months of November and December.
PERENCANAAN STRATEGIS SI/TI MENGGUNAKAN FRAMEWORK WARD & PEPPARD: STUDI KASUS UNIVERSITAS PARAMADINA Syalevi, Rahmad; Nazief, Bobby A.A; Barcah, Quintin K. D.
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4978

Abstract

Universitas Paramadina (UPM) requires information technology (IT) infrastructure and services aligned with its vision, mission, and business processes to support its educational and research missions. Challenges in managing IT services at UPM necessitate a strategic IS/IT plan. Ward & Peppard is the approach used in this study to build an IS/IT strategic plan aligned with UPM's strategic objectives. The business model canvas, value chain analysis, critical success factor, PESTEL, mcfarlan’s strategic grid, and Gartner technology trends are employed as supporting analytical instruments. Data is collected through interviews, direct observations, and reviews of relevant documentation. Using thematic analysis and open coding methods, this research designs an IS/IT strategic plan for UPM. The results include recommendations for 8 new applications and 17 updates to existing applications, 13 IT initiatives focusing on infrastructure adjustments, and 10 IS/IT management strategies covering policy development, governance structure, data management, and IT audits. This strategic roadmap is developed for the next five years to enhance UPM's added value and competitive advantage through optimized IT.
Architectural Evolution of Transformer Models in NLP: A Comparative Survey of Recent Developments Waysi Naaman, Diyar; Berivan Tahir Ahmed; Ibrahim Mahmood Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4984

Abstract

This literature review examines the impact and advancements of XLM-RoBERTa in the field of multilingual natural language processing. As language technologies increasingly transcend linguistic boundaries, XLM-RoBERTa has emerged as a pivotal cross-lingual model that extends the capabilities of its predecessors. Through comprehensive pre-training on multilingual corpora spanning 100 languages, this model demonstrates remarkable zero-shot cross-lingual transfer capabilities while maintaining competitive performance on monolingual benchmarks. This review synthesizes research findings on XLM-RoBERTa's architecture, pre-training methodology, and performance across diverse NLP tasks including named entity recognition, question answering, and text classification. By examining comparative analyses with other multilingual models, we identify key strengths, limitations, and potential directions for future research. The findings underscore XLM-RoBERTa's significance in advancing language-agnostic representations and bridging the performance gap between high-resource and low-resource languages, with substantial implications for global accessibility of language technologies.
Pemodelan UML Sistem Keamanan Berbasis Amplop Digital untuk Memenuhi Layanan Keamanan Insan, Isa Mulia; Husen, Jati Hiliamsyah; Yasirandi, Rahmat
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4986

Abstract

This research focuses on the development and systematic redesign of a Unified Modeling Language (UML) model to represent a digital envelope-based security system. The digital envelope method, integrating both symmetric and asymmetric encryption, is employed to leverage the strengths of each encryption type, ensuring performance and security in data protection. The study enhances a previously proposed UML model by incorporating security symbols and notations tailored for security modeling, effectively capturing encryption principles while ensuring clarity and accuracy. However, several limitations were identified, particularly the lack of detailed separation between the encryption and decryption processes, which are crucial for ensuring data integrity, confidentiality, and non-repudiation. The research concludes that further development is required to refine these notations, including a clear distinction between encryption and decryption stages, and the inclusion of more detailed symbols for key management. Future work should focus on extending the notations to better address the security challenges faced by digital envelope-based systems, enhancing their representation of key generation, storage, and distribution
Enhancing Arabic Extractive Summarization with TF-IDF-Weighted AraBERT Sentence Embeddings and Semantic Clustering R. Naji, Wadeea; Suresha; Fahd A. Ghanem
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4999

Abstract

The increasing amount of textual content across digital platforms, including social media, news and education, has made it difficult for users to extract useful information efficiently. Therefore, Automatic Text Summarization (ATS) becomes an essential tool for distilling large amount of information while maintaining the core idea. Progress in Arabic ATS remains limited due to the scarcity of annotated datasets, the lack of Arabic-specific NLP tools and the high computational cost of LLM. Additionally, traditional methods often fail to capture sentence-level semantics, limiting summary quality. To address this, we propose a scalable, unsupervised framework that uses TF-IDF-weighted AraBERT embeddings to generate rich sentence representations. To further capture document structure, sentences are grouped using k-means clustering. From each cluster, we identify the most representative sentences using centroid similarity and apply Maximal Marginal Relevance (MMR) as a post-processing redundancy to eliminate sentences that are too similar. Experimental evaluation on the EASC dataset demonstrates that our weighted AraBERT model outperforms traditional embedding techniques such as FastText and Unweighted AraBERT, achieving significant improvements across multiple ROUGE metrics.
Models for Studying the Impact of Statistics Characteristics of Computing Network Gridlock on the Effectiveness of Predictions using Machine Learning Al-janabi, Adel
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.5000

Abstract

The article endeavors to organize and classify a vast array of papers about contemporary approaches, strategies, and methods of data forecast across many domains, specifically with their usefulness in traffic forecast in networks of computers. The defined ordering is carried out within the context of the suggested concept model of forecast algorithms. This concept model emphasizes the qualities of both computational network activity models and traffic monitoring approaches that may be employed openly or implicitly in contemporary forecast software applications. It is demonstrated that the investigation of probabilistic characteristics for data definition, such as presence of considerable nonstationarity, certain nonlinear impacts in models of data, and the uniqueness of dissemination of data laws, could impact effectiveness in learning predictions.
Pengembangan Sistem Customer Relationship Management (CRM) Berbasis Web Dengan Workflow Dinamis Untuk Peningkatan Proses Penjualan Fadilah, Muhamad Akbar; Ratna Yulika Go; Hermansyah; Imam Sutanto
The Indonesian Journal of Computer Science Vol. 14 No. 5 (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.v14i5.4840

Abstract

The limitations of conventional static and inflexible Customer Relationship Management (CRM) systems have resulted in significant challenges in adapting to the specific needs of various types of businesses, with PerFormance being restricted in terms of flexibility to adapt to market changes. After implementing a CRM system with a static Workflow, PT Rintis Sejahtera recorded significant improvements, such as a 35% increase in customer request handling speed and a 28% increase in customer satisfaction. This research aims to develop a web-based CRM system with dynamic Workflows that can enhance operational Efficiency and effectiveness, allowing it to adapt to future changes. The study focuses on implementing web technologies to design and develop a CRM system capable of adjusting Workflows according to the specific needs of the user company. The research methodology used in this study includes the PIECES framework and the Waterfall model. The PIECES approach covers aspects such as PerFormance, InFormation, Economics, Control, Efficiency, and Service, which are applied to analyze and design a better CRM system. Additionally, the Waterfall method is used to ensure that each stage of system development, from requirements analysis to testing, is carried out sequentially and systematically. The combination of these two methods aims to produce a functional system that meets user expectations. The results of the study show that the web-based CRM system with dynamic Workflows successfully improved operational Efficiency and effectiveness by 50% in customer request handling speed and 45% in customer satisfaction. System testing was conducted using the Black Box Testing method, with the evaluated indicators focusing on the Expected Results of each system function, such as respone speed, the ability to manage dynamic Workflows, and user satisfaction levels. The test results indicate that the system is fully functional and meets all expected requirements.

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