Comparative Analysis of SVM and NB Algorithms in Evaluating Public Sentiment on Supreme Court Rulings

Abstract

The legal events that happened to Ferdy Sambo and the Supreme Court’s decision in the cassation triggered emotional reactions and various opinions among the public, especially on social media sites such as Xapps. Some comments reflect people’s concerns about fairness in the legal system. They doubted the integrity of legal institutions or believed that decisions were unfair or in line with vested interests. This research aims to analyze public perceptions of Supreme Court decisions. The research process includes data collection, preprocessing, labeling, weighting, classification using Support Vector Machine and Naïve Bayes, and performance evaluation using a confusion matrix. A dataset of 624 was taken from X apps using the Twitter scraping technique. The lexicon method is used for data labeling, dividing the data into positive, negative, and neutral classes. The analysis results show 46 tweets categorized as positive sentiment, 133 tweets categorized as negative sentiment, and 422 tweets categorized as neutral sentiment. Based on testing with a data ratio of 80:20, both SVM and NB methods show good performance. The SVM criteria showed an accuracy of 0.84, precision of 0.61, recall of 0.78, and f1-score of 0.66, while the NB criteria showed an accuracy of 0.73, precision of 0.37, recall of 0.57, and f1-score of 0.35.