<![CDATA[The 2024 Indonesian Presidential Election sparked significant public debate regarding allegations of election fraud. This study investigated public sentiment toward media reporting in response to these fraud allegations on social media platform X. This study employed a quantitative approach with sentiment analysis methods, utilizing three sentiment analysis algorithms: Support Vector Machine (SVM), VADER Sentiment, and Naive Bayes. The research involved collecting tweets related to election fraud, which were then processed using the TF-IDF method to assess the importance of words within the text. Subsequently, the data was classified to identify the sentiment expressed in the tweets. VADER achieved the highest accuracy of 100%, followed by SVM at 92.29%, and Naive Bayes at 90.05%. While most tweets were neutral, negative sentiment was more prevalent in all models. These findings suggested that social media sentiment reflected public opinion on sensitive political issues, providing valuable insights into the discourse on election fraud. The study underscored the need for improving sentiment analysis methods, particularly in addressing data imbalance and the complexities of political sentiment in Indonesia.]]>
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