Penerapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024

 (*)Zairy Cindy Dwinnie Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Rice Novita (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: February 22, 2024; Published: April 23, 2024

Abstract

The 2024 Presidential Election has become the hottest topic in the past two years. The KPU has confirmed that there are 3 candidates for President and Vice President. For this reason, as a momentum for voters to assess the 2024 Presidential and Vice Presidential candidates, the KPU is holding the 2024 Presidential Choice Debate which is based on Law Number 7 of 2017 concerning General Elections. Based on the information presented on the kpu.go.id page, the debate will be held 5 times with 3 presidential candidate debates and 2 vice presidential candidate debates. For this reason, it is necessary to carry out an analysis to find out how public sentiment is positive, negative, and neutral on Twitter towards the three candidates for President and Vice President in 2024 before and after the debate was held. The aim is to estimate public support or disapproval of the three candidate pairs. This research uses three algorithms as a comparison of classification accuracy, namely the Support Vector Machine algorithm, Random Forest, and Logistic Regression. Where the data used is tweet data on Twitter related to before and after the debate as many as 30 datasets with a total of 9000 data. From the classification results, the average accuracy obtained for the three algorithms, namely SVM and Random Forest, was 78%, and Logistic Regression was 79%. The highest polarity obtained from the classification of the three algorithms is in the positive class. This indicates that the Logistic Regression algorithm provides better performance in classifying Twitter sentiment regarding the 2024 presidential and vice presidential candidate pairs.

Keywords


Text Mining; 2024 Presidential Election Debate; Support Vector Machine; Random Forest; Logistic Regression

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