COMMENT SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL ELECTION ON X (Twitter) AFTER THE 5th DEBATE USING NAÏVE BAYES METHODS

Zharifa, Anjana Haya Atha (2025) COMMENT SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL ELECTION ON X (Twitter) AFTER THE 5th DEBATE USING NAÏVE BAYES METHODS. Tugas Akhir thesis, Informatics.

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Abstract

The presidential election in Indonesia has become a prominent topic of discussion on social media, particularly on Twitter. This platform offers a space for individuals to express their opinions on presidential candidates and election-related issues. This study aims to analyze public sentiment regarding presidential election news on Twitter using the Naïve Bayes Classifier method. The research data was collected from Twitter during the period from February 5, 2024, to February 13, 2024, encompassing a total of 2,561 comments. The research process involved several stages, including data collection, data preprocessing (which comprised cleaning, tokenization, and stemming), data labeling, and model training and testing. The processed data was subsequently classified into positive, negative, or neutral sentiment categories using the Naïve Bayes method. The results indicated that negative comments predominated, accounting for 52.9% of the total, followed by positive comments (32.6%) and neutral comments (14.6%). The Naïve Bayes model demonstrated the highest level of accuracy, with a percentage of 81%, employing a Naïve Bayes Classifier. This was followed by the Bernoulli Naïve Bayes model, with an accuracy of 80%, and the Gaussian Naïve Bayes model, with an accuracy of 76%. This study offers profound insights into public responses to the presidential election on social media, providing invaluable information for presidential candidates and their campaign teams. These findings also have the potential to contribute to our understanding of the dynamics of public opinion and the efficacy of political communication strategies in the digital era. Keywords: Presidential Election, Twitter, Sentiment Analysis, Naïve Bayes Classification, Public Opinion, Social Media.

Item Type:	Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir)
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User:	Kaprodi S1 Informatika UTY
Date Deposited:	06 May 2025 03:46
Last Modified:	06 May 2025 03:46
URI:	http://eprints.uty.ac.id/id/eprint/17815

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