Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Science and Technology
Muhammad Farhan Said
Halu Oleo University
Computer Science & IT Mathematics

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

Found 1 Documents
Search

 1 
Comparison of Kernel Functions in Support Vector Machine (SVM) Method for Phishing Website Detection Muhammad Farhan Said; Norma Muhtar; Bahriddin Abapihi; Arman Arman; Muhammad Kabil Djafar; Herdi Budiman
JOSTECH Journal of Science and Technology Vol 6, No 1: Maret 2026
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v6i1.13680

Abstract

Phishing is a rapidly growing form of cybercrime that poses a significant threat to internet users. Therefore, an effective and accurate detection method is essential. This study aims to analyze and compare the performance of several kernel functions within the Support Vector Machine (SVM) method for detecting phishing websites. The kernel functions used include linear, polynomial, Radial Basis Function (RBF), and sigmoid. The dataset is evaluated using stratified 5-fold cross-validation, with F1-score as the primary performance metric. The results show that the RBF kernel achieves the highest performance, with the best F1-score of 94.64%, followed by the polynomial kernel. The linear kernel performs reasonably well on nearly linearly separable data, while the sigmoid kernel shows the lowest and least stable performance. Based on these findings, the RBF kernel is recommended as the most suitable choice for phishing website detection using the SVM method. 
Co-Authors Arman Arman Bahriddin Abapihi Herdi Budiman Muhammad Kabil Djafar Norma Muhtar