Sentiment Analysis of Indonesia 2024 Election with a Comparison of Naive Bayes and KNN Algorithms on Twitter

Mespin Andayani, Fitri Marisa, Rangga Pahlevi Putra

© 2024 Fitri Marisa, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International. (CC BY-NC 4.0).

Citation Information: SAR Journal. Volume 7, Issue 3, Pages 204-212, ISSN 2619-9955, https://doi.org/10.18421/SAR73-06, September 2024.

Received: 16 July 2024.
Revised:   05 September 2024.
Accepted: 12 September 2024.
Published: 27 September 2024.

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.

Keywords – sentiment analysis, SIREKAP, naïve bayes, K-nearest neighbors, election.

                   

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