Komparasi Feature Selectiontf-Idf Dan Chi Square Pada Klasifikasi Berita Online Di Indonesia Dengan Metode Multinomial Naïve Bayes

Labata, Ahira (2020) Komparasi Feature Selectiontf-Idf Dan Chi Square Pada Klasifikasi Berita Online Di Indonesia Dengan Metode Multinomial Naïve Bayes. Undergraduate thesis, POLITEKNIK NEGERI JEMBER.

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Abstract

ABSTRACK As a result of the increasing of the internet use, nowadays, for accessing news, many people move to online media such as websites or news applications which are always updated at any time and can be accessed anytime and anywhere. With so many news uploaded on the internet, it is necessary to organize news by classifying them based on the discussion in the news. This research is to compare the online news classification system in Indonesia with Feature Selection TF-IDF and Chi Squareusing theMultinomial Naïve Bayes Method. The result is that the TF-IDF produces more accurate results with an accuracy of 90%. Keyword: news classification, multinomial naïve bayes, tf-idf, chi square, text mining

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