Komparasi Naïve Bayes dan K-NN Dalam Analisis Sentimen di Twitter Terhadap Kemenangan Paslon 02

Azizah, Alfira Fitri Nur
Ramadhan, Viry Puspaning

J-Intech (Journal of Information and Technology)
Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology

The 2024 Presidential and Vice Presidential Election stands out as a highly awaited political event by the people of Indonesia. The vote counts result, or real count, of the 2024 election have sparked a variety of reactions, both supportive and opposing, especially on social media platforms like Twitter, due to the lead of candidate pair number 02. This study utilizes Twitter as a data source for opinion interpretation. The Naïve Bayes and K-NN were chosen in this study, and their performances are tested and compared. The research results present Naïve Bayes with an accuracy rate of 87.35% +/- 1.81% (micro average: 87.35%), while K-NN algorithm achieved an accuracy rate of 69.68% +/- 3.14% (micro average: 69.68%) using a data partition ratio of 90:10. The analysis results indicate that Naïve Bayes is more effective than K-Nearest Neighbor.