CLASSIFICATION OF NEWS CONTENT BY TEXT MINING

Abstract

Information has become a necessity in human life. Information is said to be knowledge gained from learning, experience, or instruction. In some cases, knowledge about events or situations that have been collected or received through the process of communication, intelligence gathering, or obtained from the news is also called information. Text Mining is the application of data mining concepts and techniques to look for patterns in text, which is the process of analyzing text to include information that is useful for a particular purpose. Based on the results of the implementation and trials that have been carried out, it can be concluded that the results of this study have resulted in a classification system of news articles on categories namely sports, technology, economics, and politics. The news article classification system is done by using the naive bayes classifier algorithm to receive data input in the form of article text data that is processed with text mining, namely the casefolding, tokenizing and filtering process. The accuracy in determining the categories in the new article data is influenced by learning data or training data in each category.