Document Classification using Naïve Bayes for Indonesian Translation of the Quran

Classification for Indonesian language documents was increased. But the application of classification for question and answer system needs is still few. The purpose of this paper is to maximize the classification of Indonesian documents especially the Qur'an translation to support the question and answer system. In the process of creating a question and answer system that is still ongoing, testing the Naïve Bayes algorithm becomes very important besides other algorithms. The Naïve Bayes method is the first choice in this test as it has practicality in calculating. The result of this study is the classification of ITQ documents with 4 categories: morality, faith, knowledge, and Muamalah. The average accuracy rate of 90.5% indicates that the Naïve Bayes method is still relevant for use.