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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 7 Documents
 1 
Search results for , issue "vol. 4 no. 2 (2026)" : 7 Documents clear
SYSTEMATIC REVIEW OF THE UTILIZATION OF ARTIFICIAL INTELLIGENCE IN FORENSIC DENTISTRY AS A ROLE MODEL FOR IMPLEMENTATION AT RSAL DR MINTOHARDJO Fredy Budhi Dharmawan; Yun Mukmin Akbar; Mohammad Ali Nugroho; Ahmad Faisol; Rithy Vann
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.v4i2.3615

Abstract

Forensic odontology plays a critical role in human identification, yet conventional methods remain time-consuming, subjective, and limited in handling large-scale data, particularly in disaster and military contexts. This study aims to systematically review the utilization of artificial intelligence in forensic odontology and to develop a contextual role model for implementation at RSAL dr Mintohardjo. A systematic review design was employed by analyzing peer-reviewed articles from major databases published between 2014 and 2025 using predefined inclusion criteria and thematic synthesis. Findings indicate that artificial intelligence, especially deep learning models, significantly improves accuracy, efficiency, and scalability in dental identification, age estimation, and bite mark analysis, with performance often exceeding ninety percent under controlled conditions. Results further reveal that successful implementation depends on data quality, interdisciplinary collaboration, and institutional readiness, while challenges include ethical concerns, data limitations, and lack of standardized protocols. The study concludes that artificial intelligence has strong potential to transform forensic odontology practices and can serve as a strategic role model for institutional adoption, provided that technological integration is aligned with infrastructure, human resources, and governance frameworks. Implications extend to policy development, capacity building, and future research directions emphasizing real-world validation and sustainable implementation strategies in complex healthcare environments globally
EDGE COMPUTING AND REAL-TIME DATA PROCESSING: OPTIMIZING LATENCY AND EFFICIENCY IN INTERNET OF THINGS (IOT) ECOSYSTEMS Galih Praditya Purnomo; Supriadi Supriadi; Mochamad Achnaf; Amir Raza
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.v4i2.3621

Abstract

The rapid expansion of Internet of Things (IoT) ecosystems has intensified the need for efficient real-time data processing, exposing limitations of cloud-centric architectures in handling latency-sensitive applications. Increasing data volumes, network congestion, and delayed response times have highlighted the necessity of decentralized computing approaches. This study aims to examine the effectiveness of edge computing in optimizing latency and system efficiency within IoT environments. A mixed-methods experimental and simulation-based design was employed, comparing edge-based, cloud-based, and hybrid architectures across multiple application scenarios. Performance metrics including latency, throughput, energy consumption, and bandwidth utilization were analyzed using statistical and comparative techniques. Findings indicate that edge computing significantly reduces latency and energy consumption, while hybrid architectures achieve optimal throughput and scalability. Bandwidth utilization emerges as a key mediating factor influencing system performance, with decentralized processing improving responsiveness under high network load conditions. The study concludes that edge computing provides a robust and adaptive solution for enhancing real-time data processing in IoT ecosystems, particularly when integrated with cloud systems through optimized task allocation strategies. Effective deployment requires context-aware design, efficient resource management, and alignment with application-specific requirements.
OPTIMIZATION OF KSM MAJU JAYA OPERATIONS THROUGH INFORMATION TECHNOLOGY INNOVATION FOR DATA MANAGEMENT AND SALES OF ENVIRONMENTALLY FRIENDLY PRODUCTS Indra Satriadi; Ahmad Zarkasih; Krisna Natawijaya; Zhang Li
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.v4i2.3668

Abstract

Community-based waste management through Kelompok Swadaya Masyarakat (KSM) Maju Jaya plays a vital role in the circular economy by transforming organic refuse into eco-friendly products like natural dish soap. Manual recording systems and conventional marketing strategies currently hinder operational efficiency, leading to data inaccuracies and limited market reach. This research aims to optimize KSM operations by developing an integrated web-based management information system focused on data management and sales. The study employs a Research and Development (R&D) methodology, incorporating user requirement identification, iterative UI/UX prototyping using Figma, and functional verification through blackbox testing. Results demonstrate that the developed system provides a systematic, transparent, and accurate platform for tracking production, stock, and transactions. The web-mobile responsive design ensures accessibility across various devices, significantly reducing human error and accelerating report generation. This research concludes that digital transformation through user-centric information technology effectively enhances the institutional capacity and sustainability of community-led environmental initiatives. Implementation of this system serves as a scalable model for other grassroots organizations transitioning toward professionalized digital management.
SOFTWARE DEFINED NETWORKING ARCHITECTURE FOR ENTERPRISE COMPUTER NETWORKS PERFORMANCE OPTIMIZATION Miku Fujita; Daiki Nishida; Setiawan Setiawan
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.v4i2.3783

Abstract

The increasing complexity and demands of modern enterprise networks have highlighted the limitations of traditional network management systems. Network performance issues such as latency, congestion, and inefficient resource allocation have become significant challenges for businesses that rely on high-speed, reliable communication. Software Defined Networking (SDN) architecture has emerged as a promising solution to these challenges, offering centralized control, flexibility, and real-time optimization. This study investigates the impact of SDN architecture on the performance optimization of enterprise computer networks. The research aims to assess how SDN can improve network throughput, reduce latency, and enhance overall network efficiency in large-scale enterprise environments. A mixed-methods approach was employed, using both quantitative performance metrics and qualitative feedback from network administrators and IT managers. The results show a significant improvement in key network performance indicators, with throughput increasing by 30%, latency decreasing by 25%, and packet loss reducing by 18% after the implementation of SDN. The study concludes that SDN is a highly effective approach for optimizing network performance, offering greater scalability, security, and efficiency in enterprise settings. SDN enables real-time, dynamic network management, making it a crucial technology for modernizing enterprise network infrastructure.
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.
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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