<![CDATA[The Independent Curriculum is a strategic education policy that aims to increase learning flexibility and develop student competencies in the 21st century. This research focuses on analyzing public sentiment towards the implementation of the Independent Curriculum using two machine learning algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). One of the main challenges in this study is the imbalance of sentiment data that includes negative, neutral, and positive classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) technique was applied to balance the distribution of data between classes. The results show that the SVM method is superior to KNN with an overall accuracy of 92% and a high F1-score in the majority class (Neutral: 96%), although the performance in the minority class (Negative: 43% and Positive: 40%) still needs improvement. In contrast, the KNN method recorded a lower overall accuracy of 31% but had a more even distribution of errors. After the implementation of SMOTE, there was a significant improvement in both methods, especially in recognizing minority classes. This study concludes that SVM is more effective for sentiment classification tasks on datasets with class imbalances, and recommends further exploration of ensemble methods to improve the quality of prediction and model generalization.]]>
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