<![CDATA[This study investigates public sentiment toward the first 100 days of the Prabowo–Gibran administration by analyzing opinions expressed on X (formerly Twitter) using machine learning approaches. A total of 431 valid tweets were collected, preprocessed, and manually labeled into positive and negative categories. The results reveal that 62% of public sentiment was negative, while 38% was positive, indicating widespread public criticism during the administration’s early period. Two algorithms, Naïve Bayes and K-Nearest Neighbor (KNN), were applied to classify sentiment. The Naïve Bayes model achieved superior performance, with an accuracy of 97.22%, compared to KNN’s 62.65%. The probabilistic nature of Naïve Bayes allowed it to manage high-dimensional, imbalanced textual data effectively, while KNN suffered from the “curse of dimensionality” and class bias. These findings demonstrate that Naïve Bayes remains a reliable and computationally efficient model for political sentiment analysis in the Indonesian digital context. Despite its strengths, this study acknowledges limitations in manual labeling and linguistic nuances such as sarcasm and irony. Future research is encouraged to integrate deep learning architectures like LSTM or BERT and adopt aspect-based sentiment analysis to capture more contextual insights from political discourse.]]>
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