Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification

Abstract

One of the social media crimes that is rampant in the current era is cyberbullying. Cyberbullying is a form of intimidation by someone to harass other people using technological devices. this research uses a design for information decision making that aims to get the expected results. the data collection process is carried out manually with a time frame of 1 week by watching the live broadcast of the online game YouTube streamer then sorting out some bullying and non-bullying comments in the comment’s column. Data labeling is done manually. The data obtained amounted to 1000 with 500 negative comments and 500 positive comments. The above test can be concluded that from the distribution of test data there are 90% - 10% have results that are superior to the results of other tests with an increase of 4% in the Naïve Bayes weighting Gain Ratio method. Based on the test data, the results of precision, recall, F1-score and accuracy of the Naïve Bayes classification method are obtained. The test analysis above can be concluded that from the distribution of test data, 90% - 10% have results that are superior to other test results with a 4% increase in the Naïve Bayes weighting Gain Ratio method. The existence of increased accuracy results is due to a randomized data processing process.