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All Journal Prosiding National Conference for Community Service Project Journal of Computer Networks, Architecture and High Performance Computing
Kevin Chandra Wijaya
Universitas Internasional Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education

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Sosialisasi Pemilih Pemula SMAN 4 Batam oleh Mahasiswa Universitas Internasional Batam Abdurrahman Alhakim; Winda Fitri; Novri Winson; Marsya Asyikin; Ar-Raudah Ar-Raudah; Melisa Melisa; Della Delia; Dylan Perdinando; Christofer Paskah De La Cruz Nongsina; Kevin Chandra Wijaya; Muhammad Firza Herianto; Karen Amaris; Ferinna Lidya; Jason Jason; Shely Fitria Binti Adi Azhar; Elisabeth Ronauli Ina Sedo Langodai; Permata Andini Sinaga; Yehezkiel Christian Angga Anjula Purba; Angelina Oei; Williem Aditya Sion Purba
National Conference for Community Service Project (NaCosPro) Vol 5 No 1 (2023): The 5th National Conference for Community Service Project 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v5i1.8292

Abstract

Sosialisasi pemilu pemula yang diadakan di SMAN 4 BATAM pada 27 Juli 2023 ini memliki tujuan untuk memberikan pengajaran dan pengarahan kepada siswa-siswi dalam mengikuti prosedur pemilu yang akan terlaksana pada bulan Februari 2024 nanti. Kegiatan ini juga bertujuan untuk memberikan pemahaman tentang sistem demokrasi yang ada di Indonesia.
An Optimized Lightweight CNN with Randomized Hyperparameter Search for Real-Time Image-Based Malware Detection Stefanus Eko Prasetyo; Kevin Chandra Wijaya; Haeruddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Articles Research 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 Abdurrahman Alhakim Angelina Oei Ar-Raudah Ar-Raudah Christofer Paskah De La Cruz Nongsina Della Delia Dylan Perdinando Elisabeth Ronauli Ina Sedo Langodai Ferinna Lidya Fitri, Winda Haeruddin Jason Jason Karen Amaris Marsya Asyikin Melisa Melisa Muhammad Firza Herianto Novri Winson Permata Andini Sinaga Prasetyo, Stefanus Eko Shely Fitria Binti Adi Azhar Williem Aditya Sion Purba Yehezkiel Christian Angga Anjula Purba