<![CDATA[The rapid growth of social media has made digital platforms a primary space for expressing public opinion on government policies, including the Free Nutritious Meal Program (MBG). TikTok, as a widely used platform, allows users to share opinions openly through comments. This study aims to analyze public sentiment toward the MBG program based on TikTok comments using the Support Vector Machine (SVM) algorithm. Relevant comments were collected and classified into positive, neutral, and negative categories. The data then underwent preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Text data were transformed into numerical form using the TF-IDF method. The dataset was split into training and testing data with an 80:20 ratio. Results show that most comments are positive (60.64%), followed by neutral (32.94%) and negative (6.41%). The highest accuracy (79.71%) was achieved using linear and sigmoid kernels, indicating SVM’s effectiveness for sentiment analysis.]]>
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