A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION

  • AAIN Eka Karyawati (1*) Udayana University
  • Komang Dhiyo Yonatha Wijaya (2) Udayana University
  • I Wayan Supriana (3) Udayana University

  • (*) Corresponding Author
Keywords: Spam, SVM, Kernel Function, Classification

Abstract

Today, the use of e-mail, especially for formal online communication, is still often done. There is one common problem faced by e-mail users, which is the frequent receiving of spam messages. Spam messages are generally in the form of advertising or promotional messages in bulk to everyone. Of course this will cause inconvenience for people who receive the SPAM message. SPAM e-mails can be interpreted as junk messages or junk mail. So that spam has the nature of sending electronic messages repeatedly to the owner of the e-mail. This is abuse of the messaging system. One way to solve the spam problem is to identify spam messages for automatic message filtering. Several machine learning based methods are used to classify spam messages. In this study, a comparison was made between several kernel functions (i.e., linear, degree 1 polynomial, degree 2 polynomial, degree 3 polynomial, and RBF) of the SVM method to get the best SVM model in identifying spam messages. The evaluation results based on the Kaggle 1100 dataset showed that the best model were the SVM model with a linear kernel function and a degree 1 polynomial, where both models returned Precision = 0.99, Recall = 0.99, and F1-Score = 0.98. On the other hand, the RBF kernel produced lower performance in terms of Precision, Recall, and F1-Score of 0.95, 0.95, and 0.94, respectively.

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Author Biographies

Komang Dhiyo Yonatha Wijaya, Udayana University

Student

I Wayan Supriana, Udayana University

Lecturer and Researcher

Published
2023-02-27
How to Cite
[1]
A. E. Karyawati, K. D. Y. Wijaya, and I. W. Supriana, “A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION”, jitk, vol. 8, no. 2, pp. 97 - 103, Feb. 2023.
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