Comparing SVM and Naïve Bayes Classifier for Fake News Detection

Nurhasanah Nurhasanah (Bina Nusantara University)
Daniel Emerald Sumarly (Bina Nusantara University)
Jason Pratama (Bina Nusantara University)
Ibrahim Tan Kah Heng (Bina Nusantara University)
Edy Irwansyah (Bina Nusantara University)

Engineering, Mathematics and Computer Science Journal (EMACS)
Vol. 4 No. 3 (2022): EMACS

Fake news has been evolving into a problem that is getting even more challenging. Technology has been misused to spread false information about many things, such as war, pandemics, and the stock market. Unfortunately, this issue is not a big deal for some people without conscious consumption of that news. Hence, being part takes a role in combating the spread of false information using the advancement of technology. This study proposed two methods of machine learning model, Support Vector Machine (SVM) and Naïve Bayes, to classify fake news. Furthermore, to assert the applicability of models by examining news articles dataset which contain two labels, reliable and unreliable news. The higher accuracy is 0.96 using the SVM model