COMPARISON OF NAÏVE BAYES AND INFORMATION GAIN ALGORITHMS IN CYBERBULLYING SENTIMENT ANALYSIS ON TWITTER - Dinda Septia Ningsih; Suryono, Ryan Randy (Informatika, Universitas Jenderal Soedirman, 2024)
In the current digital era, cyberbullying is very easy to do because access to various social media platforms is very easy to obtain. Generation Z is a generation born in the era of digital technology advancement, being one of the parties that plays a role in the increasing cases of cyberbullying. The twitter social media platform is one of the platforms that is often used as a place for cyberbullying in Indonesia. With the alarming impact, this research aims to analyze cyberbullying cases on twitter. By comparing Naïve Bayes and Information Gain algorithms, this research will provide accuracy results from tweet data containing cyberbullying content. The dataset used comes from twitter with the time span of collecting the dataset is from January 05, 2024 to January 25, 2024. The dataset is then processed to produce a clean dataset that is ready to be tested using both algorithms. In this study, testing the two algorithms using the K-fold Cross Validation technique resulted in variations in each test. In testing both algorithms, an accuracy level is obtained that indicates how successful the model is in making predictions. In simple terms, this accuracy assesses how effective the model is in predicting cyberbullying sentiment in datasets from Indonesian twitter. Testing the Naïve Bayes algorithm obtained an accuracy of 92.3%. Testing the Information Gain algorithm has an accuracy of 97.8%. From the results obtained, it can be concluded that the Information Gain algorithm gets higher accuracy than the Naïve Bayes algorithm for cyberbullying sentiment analysis on Indonesian twitter.