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 D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer
Ruhamah
Universitas Cokroaminoto Palopo
Computer Science & IT Education

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

Found 2 Documents
Search

 1 
PERANCANGAN SISTEM INFORMASI BERBASIS WEBSITE PADA SMP NEGERI 4 SABBANG Ruhamah
Jurnal Ilmiah Teknologi Informasi Vol 11 No 1 (2021): Edisi Januari 2021
Publisher : Universitas Cokroaminoto Palopo Fakultas Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (362.05 KB)

Abstract

Dalam Penelitian dengan judul “Perancangan Sistem Informasi Berbasis Website Pada SMP NEGERI 4 SABBANG” bertujuan untuk merancang dan membuat Aplikasi Sistem Informasi berbasis website pada UPT SMP negeri 4 Sabbang. Adapun Sistem Informasi yang akan dibuat adalah informasi data guru, data pegawai dan data siswa serta informasi tentang sekolah lainnya. Penelitian ini menghasilkan suatu Perancangan aplikasi system informasi berbasis website yang dapat bermanfaat dalam menunjang kegiatan akademik dan non akademik di UPT SMP Negeri 4 Sabbang. Dengan adanya Perancangan Aplikasi Sistem Informasi tersebut dapat membantu siswa, guru dan semua unsur civitas sekolah dalam mendapatkan informasi yang dibutuhkan dengan cepat dan tepat.
Comparative Machine Learning Classification for QRIS Quishing Detection Based on URL Features: English Permadi Kusuma; Muhammad Yusuf Halim; Ruhamah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8940

Abstract

The increasing adoption of the Quick Response Code Indonesian Standard (QRIS) as a digital payment method has been accompanied by the emergence of quishing, a phishing attack that exploits malicious QR codes to redirect users to fraudulent websites. This study aims to compare the performance of three machine learning classification algorithms Random Forest, Decision Tree, and Naïve Bayes—for detecting phishing URLs in a simulated QRIS quishing environment using URL-based features. The experiments were conducted using a publicly available phishing URL dataset representing simulated URLs that may be encountered after scanning malicious QR codes. Model performance was evaluated using accuracy, precision, recall, and F1-score. The experimental results show that Random Forest achieved the highest accuracy of 96.94%, outperforming Decision Tree 95.32% and Naïve Bayes 65.49%. The superior performance of Random Forest is attributed to its ensemble learning mechanism, which combines multiple decision trees to reduce overfitting, improve robustness, and provide more stable classification performance across diverse URL characteristics. This study contributes a comparative benchmark of machine learning algorithms for URL-based quishing detection and demonstrates that Random Forest is the most effective approach for supporting early phishing detection in QRIS payment systems.
Co-Authors Muhammad Yusuf Halim Permadi Kusuma