Abstract
This research focuses on improving the accuracy  of email and twitter classification. Spelling mistakes and lack of matches with bag of word causes the low accuracy in classifying. This research used naïve Bayes as a text classification algorithms. Text is divided into three categories: personal, work and family. To achieve maximum likelikehood value for  the category, a better preprocessing techniques is needed. It is necessary for the process to normalize the preprocessing and search for words that correspond to classes in the bag of word. So that the text can be classified by category or has a higher precision accuracy.
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