Abstract
Support Vector Machine (SVM) is a method of machine learning often used in classification and regression issues, especially in the classification of commentary reviews on social media such as Twitter. The Constitutional Court (MK) has the authority to resolve disputes resulting from the general election, including the 2024 presidential election. As an institution that maintains fairness and transparency in the democratic process, the Constitutional Court's decisions are often at the center of public attention and debate, especially on social media. In the 2024 general election, various allegations of fraud led to protests from several parties who felt aggrieved. The final and binding Constitutional Court's decision is expected to resolve the conflict that arises, but it often does not satisfy all parties, causing political and social tensions. This conflict can be reflected through public opinion expressed on social media, such as Twitter, where various responses and sentiments to the decision are essential analysis materials. This Research uses the Support Vector Machine (SVM) algorithm with a dataset of 1383 review comments divided by an 80:20 ratio for training and testing. The system was implemented using the Python programming language, with evaluations showing the highest accuracy at 61.00%, precision at 61.00%, and recall at 62.00%. This study aims to analyze public sentiment regarding the Constitutional Court's decision using the SVM method and identify the tendency of public opinion as positive, negative, or neutral. Through this study, it is expected that a deeper understanding of the public's perception of the Constitutional Court's decision is obtained. In addition, this Research is likely to contribute to developing sentiment analysis methods in the future and provide a basis for recommendations for the Constitutional Court in handling election result disputes better.
Published Version
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