The Detection of Bullying Against Indonesian National Team Players Using Support Vector Machine

Detection is a process to check or conduct an examination of something using certain methods and techniques. Detection can be used for various problems, for example in detection bullying, especially on social media, is a significant problem with negative impacts on mental health, especially for public figures such as Indonesian National Team players. This study aims to detect bullying comments on the Instagram platform using the Support Vector Machine (SVM) algorithm. The research dataset consists of 3,100 comments collected from the official Indonesian National Team account, which are classified into bullying and non-bullying categories. The data preprocessing stages include case folding, tokenizing, normalization, removing stopwords, and stemming. The processed data was analyzed using the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature weighting before being classified using SVM with a linear kernel and Naïve Bayes. The results showed that SVM performed better with an accuracy of 89%, a bullying category precision reaching 93%, and a recall of 83%. Meanwhile, the Naïve Bayes method produced an accuracy of 79%, with a bullying category precision of 76% and a recall of 86%. The non-bullying category in Naïve Bayes has higher precision (84%) but lower recall (72%). Thus, SVM is proven to be more effective in detecting negative comments due to a better balance between precision and recall. However, challenges such as informal language variations and data imbalance remain obstacles in the development of this model. This study contributes to the development of cyberbullying detection technology and supports the creation of a healthier social media environment.