Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Nuzululnisa, Baiq Nadila
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Komparasi Metode Random Forest Dan Support Vector Machine (SVM) Untuk Pemodelan Klasifikasi Serangan DDos Lauwl, Christoper Michael; Husain, Husain; Nuzululnisa, Baiq Nadila; Wijaya, Hartono
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6684

Abstract

The Distributed Denial of Service (DDoS) attack is a type of cyberattack that aims to render a service, network, or website inaccessible to legitimate users. This attack not only disrupts services but also causes server crashes by repeatedly sending data packets, commonly referred to as spam. DDoS attacks can be identified as traffic anomalies. The National Cyber and Crypto Agency (BSSN) recorded 403,990,813 traffic anomalies with 347 cases specifically attributed to DDoS attacks. Based on this issue, a model capable of classifying DDoS attacks is necessary. This study employs the Random Forest and Support Vector Machine (SVM) methods through the steps of data collection, dataset loading, data preprocessing, classification modeling, and performance evaluation. In the final stage, the best method between Random Forest and Support Vector Machine is determined. The results indicate that Random Forest achieved an accuracy of 99.9%, whereas Support Vector Machine obtained an accuracy of 97.0%. Therefore, it can be concluded that Random Forest demonstrates better accuracy in classifying DDoS attacks.
Co-Authors Hartono Wijaya Husain Husain Lauwl, Christoper Michael