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Dessy Santi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Naïve bayes Clasifier dalam Klasifikasi Jenis Berita Dessy Santi; Jumadil Nangi; Natalis Ransi
Foristek Vol. 10 No. 1 (2020): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (362.383 KB) | DOI: 10.54757/fs.v10i1.52

Abstract

Sometimes the classification of news categories is still an obstacle. Classification can be wrong because it is still subjective. As a result, the selected category does not match the uploaded news description. Based on these problems, the authors feel the need to make Classification of News Types with the Naïve Bayes Classifier Algorithm. The importance of this system is to be able to classify news and help news seekers to get the news they want. Based on the test results, the Naïve Bayes Classifier algorithm has a good performance for the classification of news types. This is evidenced in testing using news data taken from www.kompasiana.com, then news is classified into four categories namely politics, economics, sports, and entertainment. The classification results using 16 test news obtained an accuracy of 87.5%.
Co-Authors Jumadil Nangi Natalis Ransi