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.
This paper exposes a novel method has been developed during these 2 years. The method is named as “adjective based automatic rating system”. This method is developed to utilize the abundant availability of text on the internet for quality and performance rating purpose. The text is processed in such a way and leave only the adjectives. Semantic analysis is done by two knowledge: adjectives of performance definition and Indonesian adjectives database with its synonym-antonym relation. This research proposes several formula steps, therefore the method output is a rating score that can be tunned its scale. The experiment results have been gathered for several objects: tourism, courier service, and organization performance. With detail information in tourism object experiment, this paper cites the other experiment results as well. This paper also provides availability information of the method as Python library. The results show a high correlation score, always more than 0.9. The results also show acceptable error scores, never more than 45%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.