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All Journal The Indonesian Journal of Computer Science
Moepi, Glen
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Smart Surveillance Systems: Trends, Challenges and Future Directions Moepi, Glen
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4802

Abstract

Smart surveillance systems integrate the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and extensive data processing (big data) to enhance real-time monitoring, automated decision-making, and data analytics across multiple sectors. In security, they improve threat detection through facial recognition and pattern analysis. In power distribution, they enhance grid stability and detect unauthorized electricity usage using predictive analytics and advanced metering infrastructure (AMI). Agriculture benefits from precision farming, optimizing resource use while monitoring crops and livestock. Despite their advantages, these systems face challenges such as high implementation costs, communication limitations, data privacy concerns, and digital security risks (cybersecurity). Urban areas benefit from high-speed networks like fifth-generation wireless technology (5G) and fiber optics, yet costs and cyber vulnerabilities remain issues. In rural regions, limited internet access hinders adoption, necessitating alternatives like satellite technology and Long-Range Wide Area Network (LoRaWAN). Overcoming these challenges will drive the development of scalable, intelligent monitoring solutions, ensuring broader accessibility and efficiency in various industries.
Five-Factor Authentication System with a Track and Trace Capability for Online Banking Platforms Moepi, Glen; Mathonsi, Topside E.
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4830

Abstract

Online banking is a rapidly growing customer service platform, but increasing cyber threats require constant security improvements. This study developed an enhanced Multi-Factor Authentication (MFA) scheme with track-and-trace capabilities to mitigate risks. The proposed system includes five authentication modalities: username, password, PIN, OTP, biometrics (fingerprint or facial scan), registered smart devices, and a time-locked user location. A major feature is its ability to detect suspicious activities and send alerts via secretly obtained photos and location triangulation. Using design science methodology, three prototype schemes were developed and compared with First National Bank (FNB) and Standard Bank (STD) security systems. Evaluated with Datadog and AppDynamics APM tools, the best prototype achieved 80% security, slightly below FNB and STD’s 90%. It matched them in resource efficiency and outperformed them in response time, averaging 500 milliseconds compared to FNB’s 700 ms and STD’s 1000 ms.
Smart Surveillance Aided by Fog, Edge and Cloud Computing Architectures: A Critical Review Moepi, Glen
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.4879

Abstract

The rapid growth of the Internet of Things (IoT) has transformed intelligent surveillance systems, creating demands for real-time monitoring, efficient data processing, and reliable decision-making. This review examines the integration of cloud, fog, and edge computing as complementary paradigms for enhancing surveillance. Cloud computing provides large-scale storage, advanced analytics, and centralized management. Fog computing brings computation and networking closer to end devices, reducing latency and optimizing bandwidth. Edge computing enables immediate processing at the source, ensuring rapid responses to security-critical events. Together, these paradigms form a hierarchical, collaborative framework that balances scalability, efficiency, and reliability while overcoming the limitations of standalone approaches. Challenges such as device heterogeneity, interoperability issues, and security risks persist. Future directions include optimized resource allocation, lightweight Artificial Intelligence (AI) for fog and edge nodes, and stronger privacy-preserving mechanisms. The synergy of cloud, fog, and edge computing is expected to drive adaptive, secure, and resilient surveillance in IoT-based smart environments.
Co-Authors Mathonsi, Topside E.