SENTIMEN PUBLIK TERHADAP KONTROVERSI BYON COMBAT SHOWBIZ BERBASIS SUPPORT VECTOR MACHINE DAN NAIVE BAYES

Abstract

The controversial match result of the sportainment event Byon Combat Showbiz Vol. 6 sparked massive debates on social media. The high volume of comments filled with slang and combat sports jargon makes public opinion mapping prone to subjective bias and Out-Of-Vocabulary (OOV) problems. This study aims to objectively classify netizens' sentiment polarity and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms. The extracted dataset of 1,081 documents was processed using Custom Dictionary-based Normalization to reduce linguistic noise. Features were extracted using TF-IDF weighting, while class imbalance was handled using the Synthetic Minority Over-sampling Technique (SMOTE) strictly on the training data to prevent data leakage. The results showed that public opinion was dominated by negative sentiments at 64.5%, rooted in criticism of the referee's technical regulations. Model evaluation proved that linear kernel-based SVM had the most optimal performance with an accuracy rate of 59.45%, outperforming Naïve Bayes which reached 58.99%. The use of a custom dictionary and strict test data separation in SVM proved effective in precisely mapping public sentiment on a complex syntactic dataset.