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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.

No abstract

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%.

Information and communication technology that’s developing is one of the main triggers of the information explosion today. Nowadays, various news content is not only easy to obtain but also easy to produce through various platforms on the internet, including popular online media, such as blogs and websites. So a lot of news content on blogs and websites that are currently being circulated leads to fake news content (hoaxes) that can mislead the perception and thoughts of the readers. Therefore, it is important to develop a system that can detect the presence of fake news content to minimize the losses caused by the presence of fake news content. In this study, the Naive Bayes algorithm is proposed as a machine learning model that will be used to detect fake news content in Indonesian language online media. As a result, the global accuracy value reached 71% with recall, precision, and F1-Score values as a whole above 70% which indicates that the proposed model can detect fake news content quite well.