Naïve Bayes’s Experiment On Hoax News Detection In Indonesian Language

Faisal Rahutomo, Inggrid Yanuar Risca Pratiwi, Diana Mayangsari Ramadhani


Website and blog are popular as a media to spread news. The validity of an article of news’s can either be valid or fake. A fake article of news is usually called a hoax news article. The purpose of making hoax news is to persuade, manipulate, affect to people to do something that contradicts or prevents the right action. A hoax news usually used threats or misleading information to make them believe things that are not real. This research proposes an experiment using naïve Bayes to detect hoax news in Bahasa Indonesia. In this research, we use our own dataset consisting of a total of 600 valid and hoax articles. We asked three reviewers to conduct manual classification for our dataset. Final tagging was obtained by adopting the maximum score from the three reviewers. In our experiment, we show that naïve Bayes can classify Indonesian online news articles with term frequency feature using the PHP-ML library component’s. We obtained an accuracy is 82.6% with static testing and 68.33% with dynamic testing. We give free access to the dataset so the future research can replicate, comparing the result and make a baseline testing.

Keywords : Hoax News Detection, Naïve Bayes Classifier.


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