Comparison of Naive Bayes and Random Forests Classifier in the Classification of News Article Popularity as Learning Material
Utomo Pujianto, Ilham Ari Elbaith Zaeni, Khalida Izdihar Rasyida · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2021
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%.