<![CDATA[The dismissal of Shin Tae Yong as the coach of the Indonesian national team has triggered a variety of reactions, ranging from disappointment to relief, among Indonesian football fans. Factors such as unsatisfactory match results and internal conflicts within the team, as well as pressure from fans and the media, were the main reasons for this decision. Although this change opens up opportunities for a new coach to improve the performance of the Indonesian national team, it also raises controversy and debate. This study aims to compare the performance of Naïve Bayes, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms in analyzing sentiment related to this dismissal. The research data were obtained from the Twitter platform with a total of 4,345 tweets collected using crawling techniques. The data then underwent pre-processing stages to produce clean data. Testing was conducted to evaluate the accuracy of each model in predicting public sentiment. The test results showed that the SVM algorithm performed best with an accuracy of 78%, followed by Random Forest with an accuracy of 77%, and Naïve Bayes with an accuracy of 63% and KNN 74% before the application of Synthetic Minority Oversampling Technique (SMOTE). After optimization using SMOTE, the SVM algorithm still showed the best performance with an accuracy of 80%, followed by Random Forest with an accuracy of 79%, and Naïve Bayes and KNN with an accuracy of 72%. Based on these results, SVM proved to be the most effective algorithm in classifying sentiment related to the dismissal of Shin Tae Yong. It is hoped that the results of this study can contribute to understanding public opinion regarding the decision to dismiss Shin Tae Yong as coach of the Indonesian national team.]]>
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