Indonesian news classification based on NaBaNA

摘要

This paper focused on the classification of Indonesian news categories. News articles have the format of text, so it will be more complex and needs to be a process to prepare the data. Also, the article is accepted Indonesia language articles should be simplified into a basic word on every word, this can be done by the method of stemmer Nazief and Andriani. For the classification method used is Naïve Bayes method is commonly used for text mining. Both of these methods Naïve Bayes and Nazief-Adriani stemming (NaBaNA) will collaborate to get results with high accuracy. The results showed by Naïve Bayes classification with the support of Nazief and Andriani get higher accuracy.