The Performance of Support Vector Machine in Classifying Public Sentiment toward Student Suicide Cases

Abstract

Introduction: The rapid growth of social media has generated large volumes of user-generated content that can be analyzed to understand public responses to sensitive social issues. This study evaluates the performance of Support Vector Machine (SVM) in classifying public sentiment toward a widely discussed student suicide case based on YouTube comments. Method: A total of 5,000 comments were collected from a video on the Denny Sumargo YouTube channel using the YouTube Data API and categorized into positive and negative sentiments. Text preprocessing included cleaning, normalization, tokenization, stop-word removal, and stemming. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance. The dataset was divided into 80% training and 20% testing data, and SVM was applied for binary sentiment classification. Results and Discussion: The SVM model achieved 99.96% training accuracy and 89.25% test accuracy, with precision, recall, and F1-score consistently around 89%. These results indicate that the TF-IDF, SMOTE, and SVM pipeline effectively classified Indonesian social media comments despite the linguistic complexity of discussions surrounding sensitive issues. Conclusion: SVM demonstrates effective and robust performance for classifying public sentiment in Indonesian YouTube comments and provides a useful approach for analyzing public responses to sensitive social phenomena.