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Journal of Computer Science Advancements
Published by Yayasan Adra Karima Hubbi
ISSN : 30263379     EISSN : 3024899X     DOI : https://doi.org/10.70177/jsca
Core Subject : Science,
Computer Science & IT
Journal of Computer Science Advancements is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the Journal of Computer Science Advancements follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 114 Documents
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BIG DATA ANALYTICS FOR SUSTAINABLE GREEN SUPPLY CHAIN MANAGEMENT OPTIMIZATION MODELS Zain Nizam; Rashid Rahman; Muhammad Arif Abdul Hakim
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3789

Abstract

The growing need for sustainable practices in global supply chains has driven the adoption of Big Data Analytics (BDA) to optimize performance and reduce environmental impact. Traditional supply chain management systems often fail to balance operational efficiency with sustainability goals, leading to increased waste and resource inefficiency. Big Data Analytics, by providing real-time insights, predictive models, and data-driven decision-making, offers a solution to this challenge. This research explores the application of BDA in the optimization of Sustainable Green Supply Chain Management (GSCM) models, focusing on how data-driven strategies can enhance both environmental and operational performance. The study employs a mixed-methods approach, combining case studies, performance metrics, and interviews with key industry stakeholders to assess the impact of BDA on supply chain efficiency, resource utilization, and waste reduction. The results show that BDA significantly improves key performance indicators, including a 20% increase in resource efficiency, a 25% reduction in waste, and a 15% decrease in operational costs. The study concludes that BDA is a crucial enabler for sustainable supply chains, providing organizations with the tools to optimize operations while minimizing their environmental footprint.
ENTERPRISE INFORMATION SYSTEMS ARCHITECTURE SUPPORTING E BUSINESS AND E GOVERNMENT DIGITAL TRANSFORMATION Ryan Teo; Fatimah Fahurian; Triyugo Winarko
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3782

Abstract

The digital transformation of both e-business and e-government is increasingly dependent on the effective implementation of Enterprise Information Systems (EIS) architecture. As organizations seek to optimize their operations, enhance transparency, and improve service delivery, the need for robust EIS architectures has become critical. These architectures facilitate the integration of diverse systems, ensuring interoperability, scalability, and security. This study investigates how EIS architecture supports the digital transformation efforts in e-business and e-government, focusing on its impact on operational efficiency, data management, and stakeholder trust. A qualitative research design is employed, utilizing case studies, interviews, and document analysis from both public and private sector organizations. The findings reveal that EIS architecture significantly enhances the operational efficiency of both e-business and e-government, improving data security and reducing administrative bottlenecks. Institutions with fully integrated EIS reported improvements in service delivery, stakeholder satisfaction, and overall transparency. The research concludes that adopting modern EIS architectures is essential for successful digital transformation, particularly in sectors where data integrity and governance are paramount.
ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTIVE ANALYTICS USING BIG DATA MINING TECHNIQUES Soleman Soleman; Ahmed Al Harthy; Mirza Ilhami
Journal of Computer Science Advancements Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i3.4104

Abstract

Rapid digital transformation has generated unprecedented volumes of heterogeneous data, creating significant opportunities for predictive analytics while simultaneously increasing challenges related to data quality, scalability, computational complexity, and decision reliability. Conventional predictive models frequently experience performance degradation when processing high-dimensional and continuously evolving Big Data environments. This study aimed to develop and evaluate an integrated Artificial Intelligence framework that combines advanced Big Data mining techniques with hybrid machine learning models to improve predictive accuracy, computational efficiency, and analytical robustness. Quantitative computational research was conducted using large-scale structured and semi-structured datasets processed through data preprocessing, feature engineering, dimensionality reduction, ensemble learning, deep learning, distributed computing, and hyperparameter optimization. Model performance was assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, computational time, memory utilization, and scalability. Experimental results demonstrated that the proposed hybrid framework achieved 98.63% prediction accuracy, an AUC-ROC of 0.995, substantially reduced computational time, lower memory consumption, and superior scalability compared with conventional machine learning and deep learning approaches. Statistical analyses confirmed significant performance improvements across all principal evaluation metrics. Findings indicate that integrating intelligent data mining with Artificial Intelligence enhances predictive capability by optimizing the complete analytical pipeline rather than individual algorithms alone, providing a scalable, efficient, and reliable framework for predictive analytics across diverse Big Data application domains.
BEYOND THE PERIMETER: ASSESSING THE IMPACT OF ZERO TRUST ARCHITECTURE ON NETWORK LATENCY AND SECURITY RESILIENCE IN LARGE-SCALE ENTERPRISE ENVIRONMENTS Hadi Mardiyanto; Zainal Syahlan; Isnadi Isnadi; Safiullah Aziz
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3859

Abstract

Enterprise networks increasingly confront sophisticated cyber threats and complex operational demands, rendering traditional perimeter-based security models inadequate. Zero Trust Architecture (ZTA) has emerged as a paradigm that emphasizes continuous verification, granular access control, and micro-segmentation to enhance security resilience across hybrid and large-scale environments. This study investigates the dual impact of ZTA on network latency and security outcomes, providing empirical insights into performance-security trade-offs.The research aims to evaluate how ZTA implementation affects network latency, throughput, and packet integrity while quantifying improvements in security resilience, including reductions in unauthorized access and lateral threat propagation. Insights from this study are intended to inform enterprise decision-making regarding optimized ZTA deployment. A mixed-methods approach was employed, combining quantitative measurements of latency, throughput, and packet loss across six enterprise networks with qualitative security assessments, including penetration testing and attack simulations. Data were analyzed using statistical techniques and thematic evaluation to identify patterns and interdependencies. Findings indicate that ZTA increases network latency moderately (3–7 ms) and reduces throughput minimally, while significantly enhancing security resilience, with a 70–85% reduction in successful unauthorized access attempts. Correlation analysis reveals a positive trade-off between performance impact and security improvements, emphasizing the importance of configuration optimization. Results confirm that ZTA provides robust protection without critically impairing network performance, offering practical guidance for large-scale enterprise adoption and informing future security-policy strategies.

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