Hoax Detection News Using Naïve Bayes and Support Vector Machine Algorithm

Abstract

Websites and blogs are well-known as media for broadcasting news in various fields such as broadcasting news. The validity of news articles can be valid or fake. Fake news is also known as hoax news. The purpose of making hoax news is to persuade, manipulate, and influence news readers to do things that contradict or prevent correct action. This study proposes to experiment with the Support Vector Machine and Naïve Bayes classifications to detect hoax news in Indonesian. This study uses a dataset from public data, namely news between valid news and hoaxes. The system can classify online news in Indonesian with the term frequency feature the machine vector Support algorithm and naïve Bayes classification. While the evaluation model used is the Confusion Matrix. The results of the comparison of the two models as a Support Vector Machine have an accuracy rate of 75,5%, and Naive Bayes has an accuracy rate of 88%. Therefore, for the classification of hoax news, we recommend the Naive Bayes model because it has a better level of accuracy than the Support Vector Machine.