A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification

• Main Authors: Fanny Fanny, Yohan Muliono, Fidelson Tanzil
• Format: Article
• Language: English
• Published: Politeknik Harapan Bersama Tegal 2018-05-01
• Series: Jurnal Informatika: Jurnal Pengembangan IT
• Online Access: http://ejournal.poltektegal.ac.id/index.php/informatika/article/view/828

<p>In this era, a rapid thriving Internet occasionally complicates users to retrieve news category furthermore if there are plentiful of news to be categorized. News categorization is a technique can be used to retrieve a category of news which gives easiness for users. Internet has vast amounts of information especially at news. Therefore, accurate and speedy access is becoming ever more difficult. This paper compares a news categorization using <em>k</em>-Nearest Neighbor, Naive Bayes and Support Vector Machine. Using vary of variables and through a several steps of preprocessing which proving k-Nearest Neighbor is producing a capable accuracy competes with Support Vector Machine whereas Naive Bayes producing just an average result, not as good as <em>k</em>-Nearest Neighbor and Support Vector Machine yet as bad as <em>k</em>-Nearest Neighbor and Support Vector Machine ever reach. As the results, <em>k</em>-Nearest Neighbor using correlation measurement type produces the best result of this experiment.  <br /><em></em></p>