Post-Election Sentiment Analysis 2024 via Twitter (X) Using the Naïve Bayes Classifier Algorithm

Mayasari, Yessi (2024) Post-Election Sentiment Analysis 2024 via Twitter (X) Using the Naïve Bayes Classifier Algorithm. Journal of Dinda.

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Abstract

After the 2024 election, there have been several events that have attracted attention. One of them is the camp of Ganjar who had sued the election results to the Constitutional Court (MK). Likewise, the couple from the Anies Baswedan camp also did the same thing, namely suing the results of the presidential election. He assessed that there had been fraud in the results of the presidential election and proposed to hold a re-vote. However, the Constitutional Court rejected the lawsuit from the two camps. As an Indonesian society that has the right to have an opinion, of course, these things invite various opinions and comments. Platform X is the main place for this discussion where platform X is a means for the community to respond and provide opinions and comments. This research was conducted using the Naive Bayes Classifier approach to dig deeper into public attitudes on Twitter towards the 2024 presidential election. In this study, the data that will be analyzed is public opinion related to various post-election statements or comments, especially the 2024 presidential election. In this study, data analysis was carried out by collecting tweets using a twitter data crawling process with a tweet harvest library. From the research that has been carried out using the Naive Bayes algorithm using data of 1228 tweet data, then after the processing stage it became 1134 tweet data. With 907 data training and 227 data testing. In testing the model on the training data, the accuracy was 91%. Meanwhile, the model test on the test data obtained an accuracy of 95%. Based on the performance results produced, which is an accuracy value of 95%, this is of course a fairly accurate result Keywords: Sentiment Analysis, Naïve Bayes Classifier, Political Elections, Twitter Data Analysis

Jenis Item:	Artikel
Subjects:	300 Social sciences > 340 Law
Pengguna yang mendeposit:	Mrs Siti Masitah
Date Deposited:	10 Jan 2025 06:32
Last Modified:	10 Jan 2025 06:32
URI:	http://repository.uinsu.ac.id/id/eprint/23101

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