Implementasi Algoritma Complement dan Multinomial Naïve Bayes Classifier Pada Klasifikasi Kategori Berita Media Online
Abstract
The development of computer technology and the dissemination of information via internet are increasing significantly from time to time. News is one of the information media which also increased. Conventional printed media has now been replaced by electronic media known as online news portals/ media. PT Merah Putih Media is one of developing online news media. There are three main categories (Lifestyles, Sports, and Indonesia) in its portal that still categorized manually by the editor in chief within the company. This study tried to test the suitability of two classification algorithms that able to replace the manual process, namely Multinomial Naïve Bayes (MNBC) and Complement Naïve Bayes (CNBC). Moreover, experiments related to the combination of both of algorithms was also tried. Based on series of experiments which had conducted, we found that the combination of MNBC and CNBC models are able to achieve the F1-Score of 90.13%.
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