<![CDATA[Social media provides a space for the public to express their views on various public policies, including the implementation of zoning regulations in PPDB. This study aims to evaluate public opinion on the PPDB zoning system in Karanganyar Regency by analyzing comments on platform X using a Naive Bayes classification method. The dataset utilized in this research was obtained through web scraping with Python in Google Colab, which focused on public comments regarding the application of the PPDB zoning policy. Before classification, the text data underwent preprocessing, including text cleaning, converting to lowercase, word separation, removing common words, and stemming to improve data quality and facilitate analysis. Then, word weighting was carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) method, followed by sentiment classification through a Multinomial Naive Bayes classifier. This study also utilized the SMOTE method to handle the imbalance in the amount of sentiment data to make the data distribution more balanced. The test results showed that the model achieved an accuracy rate of 92.77%, with a precision value of 75.00%, a recall rate of 37.50%, and an F1-measure of 50.00%. Based on the analysis, the majority of public comments regarding the PPDB zoning system in Karanganyar tended to be negative. The results of this research are expected to serve as a consideration to the Karanganyar Regency Education and Culture Office in improving the application of the PPDB zoning policy to make it fairer, transparent, and responsive to community needs.]]>
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