<![CDATA[The government's policy regarding salary increases for both civil servant (ASN) and non-civil servant (honorary) teachers in 2025 has generated various responses from the public, especially on social media. This issue has sparked public debate, with the emergence of both positive and negative comments, particularly on the Instagram platform. This study employs the Support Vector Machine (SVM) approach to classify public sentiment based on Instagram comments. A total of 1,500 comments were collected from the @folkative account during December 2024. The data were analyzed through several preprocessing stages (cleaning, case folding, tokenization, filtering, stopword removal, and stemming), followed by TF-IDF word weighting, normalization, and SVM model training and testing with an 80% training and 20% testing data split. The developed model demonstrated excellent performance, achieving an accuracy of 86%, precision of 87%, recall of 99%, and F1-score of 93%. These results indicate that the SVM algorithm is effective in classifying public opinion on government policies. This research also contributes to the advancement of machine learning applications in policy analysis based on public opinion, which can serve as valuable input for formulating more responsive policies.]]>
