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All Journal Babakti : Journal of Community Engangement Digitus : Journal of Computer Science Applications Novatio : Journal of Management Technology and Innovation Civitas: Jurnal Pengabdian Masyarakat
Arainy, Corizon Sinar
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Social Sciences Other

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Journal : Digitus : Journal of Computer Science Applications

 1 
The Role of Edge Computing in Secure and Scalable IoT Systems: A Global Perspective Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 3 No. 1 (2025): January 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i1.856

Abstract

Edge computing has emerged as a pivotal paradigm for optimizing performance, privacy, and deployment within Internet of Things (IoT) ecosystems. This narrative review aims to synthesize the latest scholarly insights into how edge computing addresses key challenges in latency reduction, data security, and resource orchestration. Drawing on a structured literature search from major academic databases, the review analyzed empirical and theoretical contributions spanning various edge-IoT implementations. The findings indicate that edge computing enhances system responsiveness by relocating data processing to proximity of data sources, leading to improved latency and throughput. In applications such as smart cities and remote healthcare, this shift enables more efficient bandwidth usage and timely decision-making. Moreover, privacy-centric technologies including federated learning, blockchain, and zero-trust architectures have proven effective in mitigating data security risks across distributed environments. Despite these advantages, systemic challenges persist, particularly regarding policy, infrastructure, and organizational readiness. Deployment in developing countries often encounters limitations due to regulatory ambiguity and insufficient digital capacity. Successful strategies observed globally emphasize the importance of hybrid cloud-edge-fog architectures and localized deployment models aligned with regional capabilities. This study underscores the need for collaborative public-private innovation, policy reform, and inclusive digital infrastructure development to fully realize the benefits of edge computing in diverse IoT contexts.
Real Time Mobility Intelligence: Evaluating Kafka Based Pipelines in Global Smart Transit Systems Sugianto; Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.959

Abstract

Real-time streaming architectures are redefining the landscape of urban transit analytics by enabling low latency, data driven decision making. This study evaluates and compares the real time data processing capabilities of public transit systems in London, New York, and Singapore. The objective is to determine how architectural choices, data freshness, and machine learning integration influence key performance indicators such as latency, ETA accuracy, and anomaly detection. The methodology involves a multi city case study, where Kafka based pipelines integrated with Apache Flink and Spark were assessed for ingestion, processing, and service delivery. Datasets included GTFS Realtime, SIRI feeds, and contextual APIs (e.g., speed bands and crowd density). Metrics for evaluation included feed latency, mean absolute error (MAE) and root mean square error (RMSE) for ETA, and response times for anomaly detection. The results demonstrate that Singapore’s transit system outperformed its counterparts with the lowest latency (~12s), highest ETA accuracy (MAE = 18s; RMSE = 25s), and superior anomaly detection via multi sensor fusion. London and New York, while technologically robust, faced constraints due to longer feed update intervals and integration complexities. Kafka ML's online learning enhanced model adaptability, significantly reducing ETA prediction errors across dynamic conditions. Furthermore, stress testing revealed Singapore’s architecture as the most resilient under peak load. The study concludes that the effectiveness of real-time urban transit systems depends on harmonizing streaming infrastructure... Singapore’s architecture may serve as a potential reference model for other cities, while recognizing contextual differences in implementation. Singapore’s architecture offers a scalable template for other cities. Ethical considerations, including data governance and passenger privacy, are essential for sustainable implementation.
Hybrid Deep Learning Models for Intrusion Detection in Cloud Networks: A Benchmark-Based Comparative Study Abdurrohman; Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i1.1116

Abstract

The increasing complexity of cyber threats targeting cloud infrastructures demands advanced and adaptive intrusion detection systems (IDS). This study explores the application of deep learning (DL) models—Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN+BiLSTM architecture—for detecting network intrusions using benchmark datasets CIC-IDS2017 and UNSW-NB15. This study contributes by demonstrating how hybrid CNN+BiLSTM architectures enhance intrusion detection accuracy on benchmark datasets, offering low latency and improved recall for rare attack classes, thereby validating their suitability for real-time cloud security deployment. Results show that hybrid CNN+BiLSTM models outperform standalone CNN and LSTM architectures in detection performance, achieving accuracies up to 97.4% on CIC-IDS2017 and 96.85% on UNSW-NB15, while maintaining acceptable latency for real-time deployment. The hybrid model also demonstrates superior F1-scores for rare attack classes and lower false positive rates. The discussion highlights the importance of dataset quality, feature engineering, and the role of adversarial training and model optimization in enhancing robustness. In conclusion, this work affirms the value of hybrid DL architectures for cloud-based IDS and suggests future directions in federated learning, adaptive retraining, and deployment in edge environments.
Co-Authors Abdurrohman Aminuyati Diantoro, Karno Rinaldo Sakti, Essy Malays Sari Samroh Saromah Sitorus, Anwar T Soderi, Ahmad Sugianto SUNARNO Wulan, Anjelis Ratu Yamin, Muhammad Ikrar