Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics Journal of Electrical, Electronic, Mechanical, Informatic and Social Applied Science
Fajar Ramadhan
Universitas Amikom Purwokerto
Arts Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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

Found 2 Documents
Search

 1 
Analisis Risiko Keamanan Website Kompetisi Nasional Menggunakan Metode Vulnerability Asessment dan CVSS 4.0 Yunan Yuga Pratama; Fajar Ramadhan; Ilham Albana; Febri Pratama
Journal of Electrical, Electronic, Mechanical, Informatic and Social Applied Science Vol. 4 No. 2 (2025): Desember 2025
Publisher : Unit Penelitian, Pengabdian Kepada Masyarakat dan Penjamnan Mutu Akademi Komunitas Negeri Pacitan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58991/qmxgvx51

Abstract

Website kompetisi nasional IITC mengelola pendaftaran dan data pribadi peserta dalam skala besar sehingga memerlukan evaluasi keamanan yang objektif dan terukur, khususnya pada fase pasca-event. Penelitian ini menganalisis risiko keamanan website IITC menggunakan metode Vulnerability Assessment yang dikombinasikan dengan validasi manual Proof of Concept (PoC) serta penilaian risiko berbasis CVSS versi 4.0. Tahapan pengujian meliputi planning and scoping, reconnaissance, vulnerability scanning, analisis dan validasi, risk scoring, serta reporting. Hasil reconnaissance menunjukkan bahwa website target menggunakan arsitektur web modern dengan layanan Cloudflare dan Netlify serta framework Next.js 13.4.4 dan React, dengan beberapa endpoint penting seperti /admin, /login, dan mekanisme redirect /dashboard. Pemindaian otomatis menggunakan OWASP ZAP 2.15.0 menghasilkan 27 kategori peringatan yang didominasi oleh temuan tingkat informasional dan low, terutama terkait konfigurasi header keamanan dan manajemen cookie. Temuan awal berisiko tinggi berupa SQL Injection dan SQL Injection–SQLite setelah dilakukan validasi PoC terbukti sebagai false positive. Hasil penilaian risiko menggunakan CVSS 4.0 menunjukkan bahwa seluruh kerentanan yang tervalidasi berada pada kategori risiko sedang, dengan skor tertinggi sebesar 6,9 pada kerentanan Content Security Policy Header Not Set dan Application Error Disclosure. Tidak ditemukan kerentanan dengan tingkat risiko tinggi maupun kritis yang dapat dieksploitasi secara langsung. Dengan demikian, postur keamanan website IITC pada fase pasca event berada pada tingkat risiko sedang, dengan fokus mitigasi diarahkan pada perbaikan konfigurasi keamanan preventif untuk mencegah potensi eskalasi serangan di masa mendatang.
Optimizing Multiclass Android Malware Family Classification Using SMOTE-Tomek Links and XGBoost Ali Nur Ikhsan; Adam Prayogo Kuncoro; Debby Ummul Hidayah; Fajar Ramadhan
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1788

Abstract

The increasing sophistication of Android malware attacks has created significant challenges for accurate malware family classification, particularly under highly imbalanced data distributions where minority malware families are frequently misclassified. This study presents a robust multiclass Android malware family classification framework by combining SMOTE-Tomek Links hybrid resampling with an optimized Extreme Gradient Boosting (XGBoost) classifier. The proposed framework addresses two critical issues in previous studies: ineffective handling of minority classes and potential data leakage during resampling and model validation. Experiments were conducted using the CCCS-CIC-AndMal-2020 After Reboot dataset containing 25,059 malware samples distributed across 14 malware families. The proposed approach applies stratified data partitioning, leakage-free SMOTE-Tomek Links integration within an imbalanced-learn pipeline, and RandomizedSearchCV-based hyperparameter optimization with 5-fold stratified cross-validation. Evaluation on an independent holdout test set demonstrates that the optimized framework achieves 80.09% accuracy, 79.85% weighted F1-score, 74.00% macro F1-score, and 97.48% OvR ROC-AUC, outperforming baseline XGBoost and Random Forest models. The results confirm that hybrid resampling combined with optimized gradient boosting improves classification reliability, especially in addressing severe class imbalance and enhancing recognition capability across diverse Android malware families.
Co-Authors Adam Prayogo Kuncoro Ali Nur Ikhsan febri pratama Hidayah, Debby Ummul Ilham Albana Yunan Yuga Pratama