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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 109 Documents
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EMBEDDED SYSTEMS DESIGN FOR SMART PRODUCTS IN INDUSTRY FOUR POINT ZERO MANUFACTURING Sujana, Nana; Tan, Jaden; Lim, Sofia
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3391

Abstract

Industry Four Point Zero manufacturing has transformed conventional production systems into intelligent, interconnected environments in which smart products play a central role. These smart products rely heavily on embedded systems to enable sensing, real-time control, communication, and autonomous decision-making under strict industrial constraints. This study aims to examine how embedded systems design influences the performance of smart products in Industry Four Point Zero manufacturing contexts, with particular attention to design attributes that support efficiency, adaptability, and reliability. A mixed-methods research design was employed, combining quantitative analysis of survey data collected from industrial practitioners with qualitative insights derived from case-based observations in manufacturing settings. The instruments focused on key embedded system design dimensions, including modularity, real-time responsiveness, communication efficiency, and system reliability, as well as corresponding smart product performance indicators. The results reveal that embedded systems design has a significant and positive effect on smart product performance, with communication efficiency and system reliability emerging as the strongest predictors of operational efficiency and fault tolerance. The findings demonstrate that smart manufacturing effectiveness is strongly determined by device-level design decisions rather than by higher-level digital infrastructures alone. In conclusion, the study highlights embedded systems design as a strategic foundation for smart products and underscores its critical role in achieving sustainable and resilient Industry Four Point Zero manufacturing.
INFORMATION SECURITY FRAMEWORK INTEGRATING CRYPTOGRAPHY FOR SECURE INTERNET OF THINGS COMMUNICATION Syahlan, Zainal; Al-Shaibani, Khalid; Al-Farsi, Layla
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3393

Abstract

The rapid growth of the Internet of Things (IoT) has introduced significant security challenges due to the increasing interconnectivity of devices and the sensitive nature of the data exchanged. Securing IoT communications is crucial to prevent unauthorized access, data breaches, and cyberattacks. However, traditional cryptographic methods often fail to meet the unique needs of IoT systems, which are constrained by resource limitations such as processing power and energy consumption. This research aims to develop a comprehensive information security framework that integrates cryptographic protocols tailored to secure IoT communications while maintaining efficiency. The study employs a mixed-methods approach, combining simulation-based experiments and expert interviews. Various cryptographic techniques, including AES, RSA, and Elliptic Curve Cryptography (ECC), are evaluated in IoT network configurations across different environments. Performance metrics such as encryption time, energy consumption, and data integrity are measured to assess the framework’s effectiveness. The results demonstrate that ECC offers the best balance between security and resource consumption, outperforming AES and RSA in terms of efficiency. Expert feedback confirms the feasibility and scalability of the proposed framework. This research contributes to the field by offering a novel approach to IoT security that can be applied to real-world networks, ensuring secure and efficient communication.
INTELLIGENT AGENT SYSTEMS FOR ADAPTIVE DECISION MAKING IN LARGE SCALE SMART ENVIRONMENTS Fariq, Aiman; Anis, Nina; Ilhami, Mirza
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3396

Abstract

The rapid growth of large-scale smart environments, including smart cities, autonomous transportation systems, and smart grids, has necessitated advanced decision-making mechanisms capable of adapting to dynamic and complex conditions. Intelligent agent systems (IAS) offer a promising solution by enabling decentralized, autonomous decision-making based on real-time data. This study explores the application of IAS for adaptive decision-making in large-scale smart environments, focusing on the challenges of scalability, resource allocation, and system responsiveness. The primary objective is to design and evaluate an intelligent agent system capable of operating efficiently in diverse, complex environments. A mixed-methods approach was used, combining simulations and real-world implementations in various smart environments, including energy grids, smart cities, and industrial automation systems. The results indicate that while IAS can perform effectively in smaller environments, performance decreases in large-scale systems due to increased agent interaction and data complexity. Scalability and adaptability remain significant challenges, with response times and resource allocation efficiency declining as the system size grows. The study concludes that further advancements are required in communication protocols and machine learning algorithms to enhance the scalability and real-time decision-making of IAS in large, interconnected systems.
WIRELESS COMMUNICATION TECHNOLOGIES ENABLING RELIABLE INTERNET OF THINGS SMART FARMING APPLICATIONS Wijaya, Hamid; Fujita, Miku; Nishida, Daiki
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3399

Abstract

The rapid expansion of smart farming systems has intensified the need for reliable wireless communication infrastructures capable of supporting Internet of Things (IoT) applications in heterogeneous agricultural environments. Ensuring stable connectivity in rural areas characterized by large coverage demands, energy constraints, and environmental interference remains a critical challenge. This study aims to evaluate wireless communication technologies and identify optimal configurations that enable reliable IoT-based smart farming operations. A mixed-method research design integrating large-scale field experiments and simulation-based scalability analysis was employed to assess LoRaWAN, NB-IoT, Zigbee, Wi-Fi, and 5G IoT modules. Reliability was measured using packet delivery ratio, latency, coverage range, scalability, and energy consumption indicators. Results indicate that no single technology achieves optimal performance across all reliability dimensions. LPWAN technologies demonstrated superior energy efficiency and wide-area coverage, while 5G achieved the lowest latency and highest throughput. Hybrid communication architectures consistently outperformed single-technology deployments, improving packet delivery ratio and operational resilience under varying environmental conditions. The study concludes that context-aware integration of complementary wireless technologies provides the most reliable and sustainable solution for smart farming IoT ecosystems.
E LOGISTICS AND DISTRIBUTED SYSTEMS FOR SUSTAINABLE SMART SUPPLY CHAIN OPERATIONS Hayati, Amelia; Ali, Zainab; Ahmad, Omar
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3408

Abstract

The rapid evolution of e-logistics and distributed systems has reshaped modern supply chain operations, offering the potential for improved efficiency and sustainability. However, the integration of these technologies within supply chains remains an underexplored area, particularly in terms of their collective impact on sustainability. This study aims to explore the role of e-logistics and distributed systems in enhancing operational efficiency while minimizing environmental impact in smart supply chains. The research employs a mixed-methods approach, combining quantitative surveys with qualitative case studies across 20 supply chain organizations in manufacturing, logistics, and retail sectors. The findings indicate that e-logistics technologies, particularly blockchain, IoT, and cloud-based systems, significantly improve operational efficiency, reduce costs, and enhance supply chain visibility. However, sustainability outcomes, such as emission reductions, showed more variability, with larger organizations achieving higher environmental benefits. The study concludes that while e-logistics and distributed systems can optimize supply chain performance, their impact on sustainability is contingent upon factors such as digital maturity, organizational integration, and resource availability. The research contributes to the understanding of how these technologies can be effectively integrated for sustainable supply chain operations and provides a framework for future implementation strategies.
NETWORK SWITCHING AND ROUTING OPTIMIZATION USING SOFTWARE DEFINED NETWORKING APPROACHES Isnadi, Isnadi; Mardiyanto, Hadi; Syahlan, Zainal
Journal of Computer Science Advancements Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i1.3432

Abstract

The rapid growth of cloud computing, large-scale data centers, and heterogeneous network traffic has exposed structural limitations in traditional distributed routing architectures. Conventional switching and routing mechanisms often lack global network visibility, resulting in suboptimal path selection, inefficient bandwidth utilization, and delayed convergence under dynamic traffic conditions. This study aims to design and evaluate a Software Defined Networking (SDN)-based optimization framework to enhance switching and routing performance through centralized programmability and adaptive traffic engineering. A quantitative experimental design was employed using network emulation across small-, medium-, and large-scale topologies. Comparative analysis was conducted between conventional routing protocols and the proposed SDN-based model. Performance metrics included throughput, end-to-end delay, packet loss rate, convergence time, and bandwidth utilization efficiency. Inferential statistical testing was applied to validate performance differences. Results demonstrate statistically significant improvements under the SDN framework, including increased throughput, reduced latency, lower packet loss, and faster failure recovery. Performance gains were more pronounced in large-scale and high-traffic scenarios, indicating strong scalability and resilience characteristics. The findings confirm that centralized control architecture fundamentally enhances routing optimization and network adaptability. SDN-based approaches provide a scalable and efficient solution for modern programmable network infrastructures.
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
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
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
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.

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