JOURNAL ARTICLE

Analisis Sentimen Media Sosial Twitter Terhadap Calon Presiden RI Tahun 2024 Menggunakan Klasifikasi Algoritma Naïve Bayes

Muhammad Makmun EffendiAhmad Turmudi ZyAsep Arwan

Year: 2024 Journal:   Journal of Computer System and Informatics (JoSYC) Vol: 5 (3)Pages: 739-746

Abstract

The progress of social media is currently being felt by many Indonesian people, one of the social media that is often used is Twitter, which is a media for posting information. Currently the viral post is the election of Presidential Candidates (capres) of the Republic of Indonesia which will be held in 2024, in line with this, the General Election Commission (KPU) is holding a presidential candidate debate which will be held on various television media in Indonesia and from the results of this debate the Indonesian people usually give opinions or comments on the debate from the positive and negative sides of the presidential candidates who appeared at that time, namely Anis, Prabowo and Ganjar Pranowo. To find out the results of sentiment towards the presidential candidates, the researchers carried out an analysis using a classification of tweets containing public sentiment towards the 2024 presidential candidacy, namely Anis, Prabowo and Ganjar with the classification method used in this research is Naive Bayes Classification (NBC). Anies Baswedan dataset 61.35% of Twitter users have negative comments and 39.65% of Twitter users have positive comments, Ganjar Pranowo dataset 59.12% of Twitter users have negative comments and 41.88% of Twitter users have positive comments, Ganjar Prabowo Subianto dataset 49.25% Twitter users commented negatively and 51.75% of Twitter users commented positively. Comparing the results of the three presidential candidates, Anies Baswedan's accuracy value is smaller than the other two candidates because Anies Baswedan has more negative comments than the other two candidates. Anies Baswedan got an accuracy value of 67.23%, Prabowo Subianto 83.42% and Ganjar Pranowo 88.15%. The amount of data affects the results of sentiment analysis, the more data the better the accuracy value obtained.

Keywords:
Naive Bayes classifier Political science Computer science Artificial intelligence

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Topics

Data Mining and Machine Learning Applications
Physical Sciences →  Computer Science →  Information Systems
Multimedia Learning Systems
Physical Sciences →  Computer Science →  Information Systems
Edcuational Technology Systems
Physical Sciences →  Computer Science →  Artificial Intelligence

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