CLASSIFICATION OF NEWS TYPES BY IMPLEMENTING ENHANCED CONFIX STRIPPING STEMMER
Keywords:News, Text Mining, Enhanced Confix Stripping Stemmer, Naïve Bayes Classifier
News has become a community need in the world. Managing a lot of news articles is not easy and takes a long time. Indonesia has various types of media platforms that display news, one of which is an online news portal. Automation systems that are capable of managing and grouping Indonesian language news articles are needed. This study designed and built a web-based application to classify types of Indonesian language news articles by implementing the Enhanced Confix Stripping Stemmer algorithm. The categories used in the system are entertainment, lifestyle, sports, technology, and economics. The data used is secondary data quoted from 2 online news portals in Indonesia. The system development method used is Rapid Application Development. The data used for testing amounts to 30 news. The average results obtained from the system accuracy test are 63%. This shows that the system performance for the classification of news types is good. The number of words in a news article is very influential during the classification process.
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