Analysis of Cyberbullying on Social Media Using A Comparison of Naïve Bayes, Random Forest, and SVM Algorithms
Abstract
Social media allows the public, especially the younger generation, to access information and knowledge or communicate with others online. Unfortunately, the phenomenon of bullying has evolved into cyberbullying, encompassing various forms of violence such as taunting, insults, intimidation, or harassment carried out by young individuals through digital technology or social media platforms. Therefore, considering the available data, there is a need for a method to classify text comments on social media, whether they fall into the category of cyberbullying or not. One of the methods used is the creation of a cyberbullying classification model using a Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes algorithms. This research aims to analyze cyberbullying in social media by comparing three different algorithms, namely Naïve Bayes, Random Forest, and SVM. The research results show that in the classification analysis, the Support Vector Machine (SVM) model performed the best, with an accuracy of 85%, precision of 79.93%, and recall of 94.29%. The Naive Bayes model also provided satisfactory results, with an accuracy of around 82.19%, precision of 81.29%, and recall of 85.10%. Meanwhile, the Random Forest (RF) model had a lower accuracy of approximately 73.15%, with a precision of 74.05% and a recall of 77.79%.
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