Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Computer Networks, Architecture and High Performance Computing Telcomatics
Chandra Wijaya, Kevin
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pengembangan Sistem Penentuan Durasi Lampu Hijau Pada Lampu Lalu Lintas Menggunakan Fuzzy Logic Yeo, Stefan; Tiffano Miracle Gaghana, Dave; Jason; Chandra Wijaya, Kevin; Stephen; Yulianto, Andik
Telcomatics Vol. 9 No. 1 (2024)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pavement is one of the most important aspects of the economy and everyday life in a city, as it is always used by people in their activities. The appearance of obstacles on the road can cause losses to both the economy and the quality of life of local residents. One form of obstacle that often appears on roads is traffic jams. This traffic jam can be caused by many things, ranging from the road condition, the number of vehicles, to a less than optimal red light system. This is where the application of fuzzy logic in a dynamic traffic light system can help to solve the problem.
An Optimized Lightweight CNN with Randomized Hyperparameter Search for Real-Time Image-Based Malware Detection Prasetyo, Stefanus Eko; Chandra Wijaya, Kevin; Haeruddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7765

Abstract

While image-based malware detection using deep learning has shown promise, existing methodologies predominantly rely on computationally expensive pre-trained architectures (e.g., VGG, ResNet) that create significant bottlenecks for real-time deployment on resource-constrained gateways. This research addresses this critical gap by proposing a streamlined, lightweight custom Convolutional Neural Network (CNN) specifically optimized for real-time operation. The novelty of this work lies in the strategic integration of Randomized Search Cross-Validation (RS-CV) to automate the discovery of an optimal configuration of filters, dense units, and dropout rates, eliminating the inefficiencies and biases of manual hyperparameter tuning. The proposed method transforms binary files into 64x64 grayscale images—reducing computational input by over 90% compared to standard architectures—which are then processed by the optimized custom network. Experimental results demonstrate the scientific significance of this approach, as the model achieved a near-perfect Area Under the Curve (AUC) of 0.9996 and identified threats with an average inference time of only 12–15 milliseconds. Out of 1,068 test samples, only 10 misclassifications were recorded, proving that a mathematically optimized lightweight model can outperform heavy ensemble frameworks in both accuracy and speed. These findings provide a reproducible framework for high-speed, front-line cybersecurity systems capable of detecting obfuscated threats in live network environments.
Co-Authors Andik Yulianto Haeruddin Jason Prasetyo, Stefanus Eko Stephen Tiffano Miracle Gaghana, Dave Yeo, Stefan