<![CDATA[This study aims to analyze public sentiment toward the LPDP alumni controversy on social media using a deep learning approach. The research data consist of YouTube user comments related to the LPDP issue, which were processed through text preprocessing and automatically labeled using IndoBERT into three sentiment classes: negative, neutral, and positive. This study compares two text representation methods, namely Word2Vec and FastText, implemented within a hybrid CNN–BiLSTM architecture. In addition, data imbalance was addressed using class weighting and undersampling scenarios, while TF-IDF-based Logistic Regression was used as the baseline model. The results show that the baseline achieved an accuracy of 0.83 but was strongly biased toward the negative class as the majority class. The CNN–BiLSTM model improved the ability to detect minority classes. Under the class weighting scenario, FastText demonstrated more stable performance with an accuracy of 0.77 and a macro F1-score of 0.62. Under the undersampling scenario, Word2Vec was more stable, achieving an accuracy of 0.68 and a macro F1-score of 0.67. These findings indicate that both text representation and imbalance-handling strategies substantially affect sentiment classification performance.]]>
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