Abstract
This research focuses on implementing the K-Means Clustering method to analyze public opinion regarding the 2024 presidential election. The K-Means algorithm, as a data mining method without direction, is used to group opinion data that has similar characteristics. The results of the cluster analysis confirmed the absence of text that was sarcastic or sarcastic in the Twitter data taken. Clusters were divided and categorized based on the text approach, and the results showed that the word "pilpres" appeared the most with a total count of 1778, while the word "ahy" appeared the least with a total count of 15. This research provides in-depth insight into public perceptions of the 2024 presidential election through analysis opinion data clusters on social media.
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