<![CDATA[The development of digital platforms in Indonesia’s music industry has triggered various debates regarding the song royalty system, particularly those related to copyright and income distribution for songwriters. Public opinions on these issues are widely expressed through Twitter, making it a valuable data source for sentiment analysis. This study aims to analyze public sentiment toward song royalty issues in the Indonesian music industry and compare the performance of Multinomial Naive Bayes and Support Vector Machine (SVM) algorithms using TF-IDF weighting. This study contributes through the implementation of semi-manual labeling, the use of a stratified 5-fold cross-validation approach, and multi-metric evaluation to obtain more representative sentiment classification results on song royalty issues in Indonesian social media. The initial dataset was collected through Twitter scraping using keywords related to song royalties and music copyright. The data were then processed through preprocessing stages, including case folding, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using a semi-manual approach, involving lexicon-based pre-labeling followed by manual verification into three sentiment categories: positive, negative, and neutral. Model evaluation was performed using stratified 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm outperformed Multinomial Naive Bayes, achieving an accuracy of 93.21%, while Multinomial Naive Bayes obtained an accuracy of 82.53%. These findings demonstrate that SVM is more effective in handling high-dimensional textual data represented using TF-IDF for Indonesian sentiment analysis. This study is expected to provide insights into public perceptions regarding song royalty issues and serve as a reference for sentiment analysis applications on Indonesian social media data.]]>
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