Analysis of Cyberbullying Level using Support Vector Machine Method

Abstract

Internet users in Indonesia is increasing in every year. The increase caused by several factors, such as the increasingly even distribution of internet infrastructure in Indonesia. The internet has a positive impact such as facilitating communication between individuals, while the negative impact of the internet is intimidation to someone or known as cyberbullying. Cyberbullying has a huge impact on mental health person, causing victim to be angry, depressed, and anxious. This research aims to measure the level of cyberbullying in Indonesia on Twitter using TF-IDF and Support Vector Machine. Classification in this study is classified into two classes, namely cyberbullying and non-cyberbullying. Twitter data used in this study were 3,344,782 tweets that resulted in a cyberbullying classification level of 34.59% and a non-cyberbullying classification level of 65.41%. The best accuracy value obtained is 85%.