Azizah, Alfira Fitri Nur (2024) Studi Komparatif Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor Untuk Analisis Sentimen Opini Publik di Twitter Mengenai Kemenangan PASLON 02 (Studi Pada Real Count Pemilu 2024). Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

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.

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