<![CDATA[The launch of a domicile-based policy in Indonesia's New Student Admission System (SPMB) has triggered various public responses, especially on social media platforms. Understanding these sentiments is essential for evaluating policy acceptance and guiding future improvements in educational governance. This study aims to analyze public sentiment toward the policy using automatic labeling techniques and machine learning classification, with a focus on identifying dominant public perceptions. The research applies the CRISP-DM methodology, consisting of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A total of 1,105 comments were collected from Instagram and YouTube via web scraping and then preprocessed using text cleaning, stemming, and tokenization. Sentiment labels were generated using three automatic methods: Meta AI, RoBERTa, and TextBlob. Classification was performed using the Support Vector Machine (SVM) algorithm with four kernel variations. The results indicate that the combination of TextBlob labeling and an SVM with the Sigmoid kernel achieved the highest accuracy (0.99), along with strong precision, recall, and F1 Scores. Word cloud visualizations revealed that positive sentiment was related to educational access and teacher appreciation, while negative sentiment focused on dissatisfaction with fairness and system transparency. In conclusion, this study demonstrates that automated sentiment analysis, when supported by proper preprocessing and class balancing, is a powerful approach to extracting meaningful insights from public discourse. The findings are expected to support policymakers in developing data-driven strategies for improving future education policies.]]>
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