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All Journal ASRO JOURNAL - STTAL International Journal of Social and Management Studies (IJOSMAS) Universal Journal of Science and Technology Journal of Computer Science Advancements
Syahlan, Zainal
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Computer Science Advancements

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IMAGE PROCESSING AND COMPUTER VISION TECHNIQUES FOR AUTOMATED SMART SURVEILLANCE SYSTEMS Syahlan, Zainal; Lim, Sofia; Wong, Lucas
Journal of Computer Science Advancements Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i6.3323

Abstract

The rapid development of urbanization and security concerns has prompted the integration of automated smart surveillance systems to enhance public safety and operational efficiency. Traditional surveillance methods often rely on human monitoring, which is prone to errors and inefficiencies. Image processing and computer vision techniques provide a solution by automating object detection, tracking, and anomaly recognition. This study aims to investigate advanced image processing and computer vision techniques for improving the performance of automated smart surveillance systems. A hybrid approach combining convolutional neural networks (CNNs), attention mechanisms, and edge computing is proposed to enhance both detection accuracy and real-time processing speed. The research employed experimental design, utilizing a dataset of 12,000 annotated image frames and 85 hours of video footage from diverse environmental conditions. Performance metrics such as precision, recall, mean average precision (mAP), and processing speed were measured. Results demonstrate that the proposed model outperforms traditional CNN models, achieving higher detection accuracy and faster processing speed. The study concludes that integrating edge computing with adaptive image processing and attention-based neural networks significantly improves automated surveillance system performance in real-world settings. These findings offer valuable insights for the development of scalable and efficient smart surveillance technologies.
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.
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.
Co-Authors Al-Farsi, Layla Al-Shaibani, Khalid Bandono, Adi Eko Sulistyono Isnadi, Isnadi Lim, Sofia Mahia, Fatkulil Aswar Mardiyanto, Hadi Nur Azis, Muhammad Setyaningrum, Leli Wong, Lucas