Comparison of Naive Bayes and Random Forests Classifier in the Classification of News Article Popularity as Learning Material
- DOI
- 10.2991/assehr.k.211020.036How to use a DOI?
- Keywords
- Naïve Bayes, Forest, Learning, Material
- Abstract
Tweentribune.com is a website that provides news texts with Lexile level information for each text. The Lexile level information feature is useful as a consideration for visitors to choose text that has a level of difficulty that matches their age. With such information, any news text on the site can be used as attractive teaching material for both teachers and students. However, the number of visits to each text varies widely. This study assumes that the popularity of each text is influenced not only by Lexile level information but also by other text characteristics. This research produces a number of engineering features that are extracted from the text to be used as a predictive attribute in classifying the popularity of the texts in question. Naïve Bayes and Random Forest are two classifiers used together with two popularity cluster scenarios based on k-means clustering. The results of the testing and evaluation phase show that the Random Forest algorithm has the best performance, with an accuracy value of 99.75%, an average recall value of 99.7%, and an average precision value of 98.7%.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Utomo Pujianto AU - Ilham Ari Elbaith Zaeni AU - Khalida Izdihar Rasyida PY - 2021 DA - 2021/10/21 TI - Comparison of Naive Bayes and Random Forests Classifier in the Classification of News Article Popularity as Learning Material BT - Proceedings of the 1st UMGESHIC International Seminar on Health, Social Science and Humanities (UMGESHIC-ISHSSH 2020) PB - Atlantis Press SP - 229 EP - 242 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.211020.036 DO - 10.2991/assehr.k.211020.036 ID - Pujianto2021 ER -