Article Per Year (5 Year)
p-Index From 2021 - 2026
1.323
P-Index
This Author published in this journals
All Journal Journal of Computer Science Advancements Journal of Noesantara Islamic Studies Journal of Selvicoltura Asean Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean
Wong, Lucas
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Physics Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Computer Science Advancements

 1 
Utilization of Big Data in Improving the Efficiency of E-Business Systems in Indonesia Nugroho, Agung Yuliyanto; Prasetio, Rachmat; Wong, Lucas; Rao, Ananya
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2251

Abstract

The rapid growth of digital technology in Indonesia has fostered the expansion of e-business systems, which in turn has generated vast volumes of data. However, many e-business platforms still face challenges in utilizing this data effectively to improve operational efficiency and decision-making. This research was conducted to explore the utilization of big data in enhancing the efficiency of e-business systems in Indonesia. The main objective of the study is to analyze how the integration of big data analytics contributes to optimizing business processes, customer engagement, and overall system performance in the Indonesian digital commerce ecosystem. A mixed-method approach was employed, combining quantitative surveys of 120 e-business practitioners with qualitative interviews involving 15 data analysts and IT managers from various sectors such as retail, fintech, and logistics. Data were analyzed using statistical tools and thematic coding to derive patterns and insights. The findings indicate that e-businesses implementing big data strategies reported a significant improvement in system responsiveness, personalized customer services, and data-driven decision-making. Moreover, big data utilization has been linked to enhanced supply chain management and real-time monitoring capabilities. Despite these benefits, challenges such as data privacy concerns, lack of skilled personnel, and high infrastructure costs remain significant barriers. In conclusion, the study confirms that the effective use of big data plays a crucial role in improving the efficiency and competitiveness of e-business systems in Indonesia. Future initiatives should focus on strengthening data governance and investing in human capital to maximize big data’s potential.
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.
GOODBYE LATENCY: WHY FUTURE MEDICAL DEVICES NEED ARTIFICIAL BRAINS Koh, Megan; Tan, Marcus; Wong, Lucas
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.3332

Abstract

The transition of medical technology from passive monitoring to autonomous, closed-loop intervention is critically impeded by the latency and power inefficiencies of traditional Von Neumann computing architectures. This study investigates the efficacy of neuromorphic hardware as a solution, aiming to validate a bio-inspired architecture capable of sub-millisecond decision-making for life-critical applications. Employing a rigorous hardware-in-the-loop simulation framework, we benchmarked a custom Spiking Neural Network (SNN) against industry-standard microcontrollers, utilizing large-scale cardiac and neurological datasets to evaluate inference speed, energy consumption, and signal fidelity. Quantitative results reveal that the neuromorphic system achieved a 94% reduction in end-to-end latency and a thirty-eight-fold improvement in energy efficiency compared to the digital baseline. The event-driven architecture successfully maintained 96.4% diagnostic accuracy while operating within a negligible thermal envelope suitable for implantation. These findings definitively establish that mimicking biological asynchronous processing eliminates fatal temporal delays, validating neuromorphic “artificial brains” as the essential technological foundation for the next generation of responsive, privacy-secure, and energy-autonomous medical implants.
Co-Authors Chan, Rachel Chiristiaan, Pemy Jiwon, Seo Joshi, Nikhil Koh, Megan Lim, Sofia Muntasir, Muntasir Nugroho, Agung Yuliyanto Olsson, Ingrid Prasetio, Rachmat Rao, Ananya Riatmaja, Dodi Setiawan Shodiq, MD Syahlan, Zainal Tan, Ethan Tan, Marcus Teo, Ryan