A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification
<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 amoun...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Harapan Bersama Tegal
2018-05-01
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| Series: | Jurnal Informatika: Jurnal Pengembangan IT |
| Online Access: | http://ejournal.poltektegal.ac.id/index.php/informatika/article/view/828 |
| Summary: | <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> |
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| ISSN: | 2477-5126 2548-9356 |