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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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Framework for Enhancing Interoperability, Data Exchange, and Security in Healthcare through Blockchain Technology Muderere, Vimbai Alice; Ndlovu, Belinda; Maguraushe, Kudakwashe
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.v14i4.4950

Abstract

The healthcare sector is changing, such as fragmentation issues, the sharing of data, and the security of protected health information. Traditional systems tend to work independently or in silos, resulting in disjointed patient records and system inefficiency. With more trusted healthcare providers, patients relying more on digital solutions than ever, the urgency for a consistent data management solution has never been greater. This systematic literature review (SLR) aims to investigate the existing framework, factors, opportunities and challenges of blockchain technology in healthcare systems. The integrative approach was done according to the PRISMA guidelines. A literature search was carried out on various electronic databases, including PubMed, IEE Xplore, and ACM Digital Library, which gave a total of 832 articles, to begin with. Based on set scale criteria, 18 studies were deemed relevant for analysis. The findings indicate that blockchain technology holds promise due to its ability to facilitate secure and easy data sharing through immutability, cryptographic methods, and the removal of centralized authorities. However, there is a challenge of interoperability, data exchange and security within the healthcare systems and other technologies. This study contributes to the body of knowledge by developing a conceptual framework that helps policymakers, researchers, and practitioners that act as guide to effectively implement blockchain technology in healthcare. The framework addresses key considerations of traditional systems, such as scalability, interoperability, security, and regulatory compliance, and offers a structured approach to resolving current challenges.
Evaluation of Selected Base Models for Technostress Detection Oladipo, Sunday; Onuiri, Ernest; Ayankoya, Folasade; Ogu, Emmanuel
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.v14i4.4724

Abstract

The widespread use of technology has led to an increase in technostress which is a phenomenon where individuals experience stress and anxiety due to their interactions with technology. As social media platforms become increasingly integral to daily life, detecting technostress from online interactions has become a pressing concern and an avenue to enrich the research in the area of detecting technostress. This study evaluates the performance of selected base models on X (Twitter data). Also, the study investigated the effectiveness of a feature extraction technique for the improvement of the model performance through data preprocessing. The study made use of the dataset of X posts (Sentiment140) obtained from the Standford University. The extracted features were used to train and evaluate four base models: Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), and Light Gradient Boosting Machine (LGBM). The performance of each model was evaluated based on accuracy, precision, recall, F1-score and Kappa statistics. The RF model outperformed other base models with accuracy, precision, recall, f1-score, and Kappa score values of 88.03%, 85.98%, 85.68%, 85.79% and 79.81% respectively. The results highlight the importance of preprocessing and feature extraction techniques in improving model performance; contributes to the development of more effective technostress detection systems and provide insights into the application of machine learning algorithms for analyzing online interactions.
RTSO: Comprehensive Framework for Real-Time Frequency Channel Occupancy and Spectrum Hole Detection Ntuli, Elesa; Du Chunling
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.v14i4.4878

Abstract

Efficient spectrum utilization remains a key challenge in modern wireless communications, especially in dynamic environments with limited spectrum availability. This paper introduces Real-Time Spectrum Optimization (RTSO), a framework that combines Geo-Location Spectrum Databases (GLSDBs) with real-time spectrum sensing to detect frequency channel occupancy and identify spectrum holes. RTSO uses advanced energy detection techniques, including Additive White Gaussian Noise (AWGN) modelling, to distinguish between idle and occupied channels accurately. It incorporates mathematical tools such as occupancy time and Frequency Channel Occupation (FCO) metrics for effective spectrum analysis. A notable feature is a revisit-time-based sensing mechanism that infers channel status during intermittent scans. Practical evaluations demonstrated improved detection accuracy, reduced false alarms, and better decision-making for dynamic access to available channels. Key performance metrics, including latency, bandwidth, and error rate, were compared with baseline methods, showing substantial gains in efficiency. This work provides a valuable contribution to cognitive radio systems and dynamic spectrum access, paving the way for more intelligent and adaptive spectrum management strategies in real-time communication networks.
A Dynamic Framework for Optimizing Spectrum Utilization and Interference Mitigation in White Space Networks Ntuli, Elesa; Du Chunling; Moshe Timothy Masonta
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.v14i4.4880

Abstract

This study presents a framework for optimizing spectrum utilization and reducing interference in White Space (WS) networks using the Interference Mitigation Decision Framework (IMDF). The IMDF combines Geo-Location Spectrum Databases (GLSDs), reactive spectrum sensing, and Software Defined Radios (SDRs) to address the limitations of traditional spectrum allocation methods. The IMDF enhances allocation, reduces interference, and improves network performance by monitoring real-time spectrum usage. Simulations comparing IMDF with traditional GLSD-based methods show a 70% bandwidth saving, compared to 40% in traditional approaches. Additionally, IMDF reduces interference events by 30%, improving Quality of Service (QoS) and mitigating Cross Network Interference (CNI). With dynamic spectrum management, IMDF achieves 70% spectrum utilization, while traditional systems only reach 40%. These results demonstrate IMDF's effectiveness in dynamic environments, offering a robust solution for wireless service demand and interference mitigation in increasingly WS networks. The IMDF’s adaptability, combined with its efficient resource management, makes it a promising framework for the future of spectrum allocation in increasingly congested network environments.
Optimization of Corn Crop Nitrogen Percentage Using Genetic Algorithm Aidil Adrianda A; Septian, Belen; M. Fauzan Ridho
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.v14i4.4954

Abstract

Corn is one of the strategic commodities in food fulfillment in Indonesia. Despite being one of the strategic commodities for food security, corn production is still far from meeting total consumption. One of the main factors for increasing yield is the availability of nutrients, especially nitrogen. This research aims to determine the optimal nitrogen percentage to maximise corn production using genetic algorithm. Simulations were conducted using the genetic algorithm method with parameters such as population size, maximum number of generations, mutation rate, as well as Bayesian approach for the crossover method and a Gaussian distribution for mutation. The results showed that the more generations used, the better the accuracy of the curve approach to the actual data, with an optimal nitrogen value of 1.506% in the 500th generation and a production yield of 227,718 bu/ac or 15.325 ton/ha.
Transformasi Digital Menuju Responsive Website Menggunakan Content Management System Maryam; Dian Purworini; Rona Rizky Bunga Chasanah; Widi Widayat; Diah Priyawati
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.v14i4.4963

Abstract

In the digital era, organizations are expected to follow with technological advancements, including the need to provide informative and engaging users.. This study aims to describe the transformation of a da'wah organization in delivering information as part of a broader digital outreach strategy. The main focus to utilize the WordPress CMS as a solution to enhance website responsiveness and user reach. A practical approach was adopted through the Web Development Life Cycle method, starting from analysis requirements to technological implementation. Key aspects considered include responsive design, content integration, and user experience. Functionality testing using the black-box method confirmed that the system performs as expected. The results demonstrate improved content accessibility, optimal website responsiveness across devices, and increased user engagement. This system can serve as a model for other organizations seeking to adopt similar technologies to expand the da'wah.
Automation and Selection Technique for Regression Testing: An Empirical Analysis Hilman, Muhammad; Mantiri, Wulan
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.v14i4.4967

Abstract

Software testing, particularly regression testing, is a process that is required when changes are made to the software or its environment to ensure that the software continues to perform as expected. Motivated by real industry needs, this study reports on the experience of transitioning from manual to automated regression testing in one of the mobile applications at PT. XYZ. Prior to this study, regression testing was conducted manually, resulting in significant costs and inherent subjectivity. Test automation is then applied to the activities of test execution and test result integration as an effort to increase test productivity and efficiency. This study aims to find an efficient testing alternative by separating the flow that runs tests related to changes from the flow that runs all tests. Based on the analysis of the tested application, each flow has its trade-offs. The results show that test automation can provide benefits for regression testing, application releases, and software engineering flow. The framework presented in this paper aims to serve as a guideline for other industrial applications with similar specifications that are also considering implementing test automation.
Comparative Security and Performance Evaluation of IPFS and Filecoin for Off-chain Blockchain Storage Mandinyenya, Godwin; Malele, Vusumuzi
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.v14i4.4968

Abstract

The increasing demand for secure, scalable, and decentralized data management in blockchain ecosystems has intensified the need for eefective off-chain storage solutions. Traditional blockchain infrastructures offer limited storage capacity, prompting the integration of decentralized protocols such as the InterPlanetary File System (IPFS) and Filecoin. While both enable distributed data sharing, they differ significantly in architecture, incentive mechanisms, and security assurances. This study presents a systematic literature review (SLR) of 35 peer-reviewed studies, combined with a technical evaluation of IPFS and Filecoin across five critical dimensions: performance, security, incentive models, integration feasibility, and application-specific suitability. Empirical findings indicate that IPFS provides faster data retrieval (average latency ~210 ms) and simpler integration, making it well-suited for low-risk, real-time data scenarios. However, it lacks native incentivization for long-term data persistence. In contrast, Filecoin offers higher data availability (~99.9%) and verifiable storage proofs via its token-based reward system, enhancing durability and auditability, albeit with increased latency and operational overhead. The analysis reveals that neither protocol alone fully addresses the security–scalability–persistence trade-off inherent in decentralized systems. Instead, the results advocate for hybrid architectures that combine IPFS’s performance strengths with Filecoin’s robust data assurance features. This paper contributes a structured decision-making framework to support the selection and deployment of context-appropriate off-chain storage models. The findings aim to guide researchers and practitioners in designing resilient, privacy-preserving blockchain infrastructures, particularly in domains where data integrity, verifiability, and long-term accessibility are essential.
Machine Learning and Transformer-based Model for Sentiment Analysis of Indonesian E-Commerce Reviews Wahyu Widyananda; Maskur; Ahmad Fauzi
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.v14i4.4980

Abstract

The growth of e-commerce in Indonesia has produced a large volume of user-generated reviews, which contain valuable knowledge for business decisions. However, analyzing this unstructured text data manually is inefficient. The purpose of this study is to improve the performance of sentiment classification on Indonesian e-commerce reviews using machine learning and transformer-based models. The test method is carried out using a public e-commerce review dataset. Three models are evaluated: Multinomial Naïve Bayes, Support Vector Machine (SVM), and IndoBERT. For machine learning models, text pre-processing is performed, and features are extracted using TF-IDF. For the transformer-based model, a fine-tuning approach is used. The results show that the IndoBERT model produces better classification accuracy than the other tested models. For the given dataset, this method obtains 94,1% in accuracy, outperforming both SVM (89,5%) and Multinomial Naïve Bayes (84,2%). The IndoBERT model, despite its higher computational cost, is the most effective for this classification task.
Text Prediction Standards for Modelling Under-Resourced Languages: A Shona Case Study Chibaya, Colin
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.v14i5.4369

Abstract

Text prediction is critical in natural language processing aiding as writing assistance. However, under-resourced languages lack support in current text prediction models. We examine research on text prediction standards towards modelling under-resourced languages. To achieve this, 806 studies were scrutinized, out of which 59 remained relevant. Key findings indicate the prevalence of N-gram, BERT, and LSTM models. A gap in the literature was noted, explaining why under-resourced languages are lagging. Precisely, data scarcity and domain specificity are the obstacles to progress in under-resourced language modeling. A potential solution to this challenge is visible, linked to the leverage of transfer learning techniques such as cross-lingual model pre-training. This way, data scarcity issues can be mitigated. We observed that N-gram models stand out in this respect. These are the most used text prediction approaches yielding outstanding perplexity scores. Our Shona text prediction prototype, RNN model achieved 83.69% accuracy with a perplexity score of 4.825. Notably, the N-gram based prototype outperformed the RNN model in all the measured categories. The development of text prediction standards will likely impact text prediction accuracy in under-resourced languages. Hopefully, research on under-resourced languages can draw insights from these standards and explore the development of tailored solutions.

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