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All Journal Journal of Computer Networks, Architecture and High Performance Computing
Abei, Fikri
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Computer Science & IT Education

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Twitter Sentiment Towards 2024 Jakarta Governor Candidates With Naïve Bayes Algorithm Abei, Fikri; Sulaeman, Asep Arwan; Suprapto, Suprapto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5358

Abstract

This study aims to analyze public sentiment towards candidates for the 2024 Governor of DKI Jakarta through the Twitter platform, with a focus on classifying positive and negative sentiment. Along with the rapid development of social media, Twitter has become the main channel for people to voice political opinions. Sentiment analysis was conducted using the Naive Bayes algorithm to classify the sentiment of tweets collected through crawling techniques during the campaign period. The data used includes user tweets, with features such as frequently occurring words, popular hashtags, and discussion topics related to each gubernatorial candidate. The results showed that the Naive Bayes algorithm provided the best performance in classifying sentiment data in the period August 1 to December 26, 2024, with the highest accuracy rate reaching 75% at a data ratio of 90:10. This research also identified challenges in sentiment classification, such as the presence of new terms in test documents that are not recognized by the training model. The findings are expected to provide a clearer picture of public perceptions of gubernatorial candidates and contribute to the analysis of political sentiment on social media
Co-Authors Sulaeman, Asep Arwan Suprapto Suprapto