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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Pengabdian kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi IPSSJ Jurnal Ilmu Komputer
Arya Adhyaksa Waskita
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : IKRA-ITH Informatika : Jurnal Komputer dan Informatika

 1 
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 Vol. 10 No. 1 (2026): IKRAITH-INFORMATIKA Vol 10 No 1 Maret 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i1.6283

Abstract

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.
Co-Authors Achmad Hindasyah AGUNG BUDI SUSANTO Ahmad Musyafa Anggai, Sajarwo Eko Kristianto Jihansyah, Muhamad Kamaludin Jumaidi Muhammad Rafif Misbahuddin Pebrianto, Juri Prasmono, Yossy Veiebrian Fitri Rafi Mahmud Zain Syarif Hidayatullah Taswanda Taryo Taufiqur Rahman, Taufiqur Thoyyibah Tanjung Trijanitra, Evan Tukiyat Tukiyat Tukiyat, Tukiyat