<![CDATA[Sentiment analysis of the performance of the Indonesian National Football Team in the comment section of the TikTok application using machine learning algorithms is the main focus of this research. With the increasing popularity of football in Indonesia and the numerous comments posted by users on the TikTok app, this research aims to evaluate public opinion on the national team's performance through sentiment analysis. Data was collected from the comment section related to the World Cup qualifying matches, with a total of 1,143 data points divided into 798 training data and 342 testing data. The methods used include preprocessing, TF-IDF weighting, and classification using KNN, SVM, and RF. The analysis results show that the Random Forest model achieved the highest accuracy of 97.30%, followed by KNN with an accuracy of 94.30%, while SVM showed the lowest accuracy of 58.77%. The analysis indicate that the Random Forest method is the most effective for sentiment analysis in this context. Results of this study to serve as an important reference in the development and improvement of the national team's performance strategies based on public opinion analysis.]]>
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