Automatic news articles classification in Indonesian language by using Naive Bayes Classifier method

Curently, internet content growth rapidly. Automatic news classification is the classification of news into a category. In this research, the classification method used is Naive Bayes wich is known as Naive Bayes Classifier (NBC). The classification process covers: case folding, parsing, stopword elimination, stemming, words weighting, and documents classification by using NBC. This research uses 250 news articles divided into 5 categories as learning documents. The trial results of this research shows that the system is able to generate such accuracy in delivering news articles classification with the average Recall value of 92.87% and Precission value of 91.16%.