Analisis Sentimen Pengguna X Terhadap Pemilihan Gubenur Dki Jakarta Tahun 2024 Dengan Algoritma Naïve Bayes, K-Nearest Neighbor Dan Decision Tree - Syarif Hidayatullah; Arya Adhyaksa Waskita; Achmad Hindasyah (IKRA-ITH Informatika : Jurnal Komputer dan Informatika, 2026)
The 2024 DKI Jakarta Gubernatorial Election is one of the main political agendas that attracts public attention in Indonesia. In this context, sentiment analysis of the Gubernatorial and Vice Gubernatorial candidates is important to understand public opinion. With the advancement of technology, especially the internet and social media platforms like X (formerly Twitter), the public can freely express their opinions regarding the candidates. This study aims to analyze public sentiment towards the three pairs of Gubernatorial and Vice Gubernatorial candidates of DKI Jakarta through tweets collected using the crawling method, resulting in 6,120 rows of data, with each candidate pair obtaining 2,040 rows of data. The accuracy of sentiment analysis was calculated using Naïve Bayes, K-Nearest Neighbor, and Decision Tree algorithms to compare the accuracy of these three algorithms with an 80:20 testing-training data split and feature extraction using TF-IDF and Transformer, implemented using the RapidMiner software. Based on the analysis results, Naïve Bayes with TF-IDF representation showed the highest accuracy for Paslon 1 at 86.76%, followed by Paslon 2 at 76.96%, and Paslon 3 at 72.79%. Meanwhile, K-NN with TF-IDF achieved the best results for Paslon 1 (67.40%) and Paslon 2 (71.32%), while Decision Tree achieved the highest accuracy for Paslon 1 at 72.55%. For the Transformer representation, the overall accuracy was lower compared to TF-IDF, with Paslon 1 achieving 56.86%, Paslon 2 at 53.43%, and Paslon 3 at 51.23%. These results indicate that TF-IDF is more effective for sentiment analysis of tweets related to the Gubernatorial candidates, with Naïve Bayes being the most accurate algorithm.