Sentiment Analysis of Indonesia 2024 Election with a Comparison of Naive Bayes and KNN Algorithms on Twitter
Sentiment Analysis of Indonesia 2024 Election with a Comparison of Naive Bayes and KNN Algorithms on Twitter
Author(s): Mespin Andayani, Fitri Marisa, Rangga Pahlevi PutraSubject(s): Language and Literature Studies, Information Architecture, Electronic information storage and retrieval, Applied Linguistics, Cognitive linguistics, Computational linguistics
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: sentiment analysis; SIREKAP; naïve bayes; K-nearest neighbors; election
Summary/Abstract: In the 2024 General Election, the Recapitulation Information System (SIREKAP) was used to capture the vote count results electronically. However, the use of SIREKAP raises various opinions in the community, both positive and negative, regarding the accuracy of the uploaded data. This study aims to analyze public sentiment towards the use of SIREKAP in the 2024 Election through Twitter data, using the Naive Bayes and KNN algorithms. The results showed that the Naive Bayes algorithm was superior with an accuracy of 93.37%, while KNN achieved an accuracy of 77.92%. The novelty of this research is to conduct sentiment analysis and provide insight into how people perceive the use of SIREKAP in the 2024 Election through Twitter data.
Journal: SAR Journal - Science and Research
- Issue Year: 7/2024
- Issue No: 3
- Page Range: 204-212
- Page Count: 9
- Language: English
