SENTIMENT ANALYSIS OF BALI 2024 GOVERNOR CANDIDATES ON X APPLICATION USING SUPPORT VECTOR MACHINE METHOD

I Gusti Ngurah Agung Deanika; I Putu Agus Eka Darma Udayana; I Nyoman Anom Fajaraditya Setiawan; Christina Purnama Yanti; Indra  Pratistha

Authors

I Gusti Ngurah Agung Deanika Institut Bisnis dan Teknologi Indonesia
I Putu Agus Eka Darma Udayana Institut Bisnis dan Teknologi Indonesia
I Nyoman Anom Fajaraditya Setiawan Institut Bisnis dan Teknologi Indonesia
Christina Purnama Yanti Institut Bisnis dan Teknologi Indonesia
Indra Pratistha Institut Bisnis dan Teknologi Indonesia

Keywords:

Sentiment Analysis, X, PILKADA BALI 2024, SVM, Google Collab

Abstract

This research is motivated by the 2024 Bali PILKADA which will be held simulataneously in each region on November 27, 2024 to elect local leader, such as Governor, regents, and mayors. The existence of this Bali PILKADA, attracted the author to conduct research analyzing public sentiment towards candidates for Governor and Deputy Governor who will run in the 2024 Bali PILKADA. Data was collected from application X (formely twitter). Application X was chosen because of its ability to connect all users to communicate with each other through text messages “tweet” and very quickly to raise topics that are being discussed by the public. This study uses SVM method of classify the sentiment of the community in the from of positive, negative, and neutral and seek high accuracy value. Data was collected by crawling tweet text using Python code on Google Collab which resulted in 1010 raw data entries then continued with the preprocessing stage, which involved data cleaning for testing purposes. The data testing was conducted with three scenarios from the 1010 data, resulting in the highest accuracy rate of 79% and 302 data of tweets as positve sentiment, 462 data as negative sentiment, and 246 data as neutral sentiment.

References

Artanto, F. A. (2024). Support Vector Machine Berbasis Particle Swarm Optimization Pada Analisis Sentimen Anggota KPPS. Jurnal Fasilkom, 14(1), 75–79. https://doi.org/10.37859/jf.v14i1.6795

Aryanta. (2024). Pemkab Badung Sabet 15 Penghargaan Pada CNN Indonesia Awards Bali, Giri Prasta Raih Special Reward. Bali.Tribunnews.Com. https://bali.tribunnews.com/2024/05/15/pemkab-badung-sabet-15-penghargaan-pada-cnn-indonesia-awards-bali-giri-prasta-raih-special-reward

Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter. Smatika Jurnal, 10(02), 71–76. https://doi.org/10.32664/smatika.v10i02.455

Handayani, A., & Zufria, I. (2023). Analisis Sentimen Terhadap Bakal Capres RI 2024 di Twitter Menggunakan Algoritma SVM. Journal of Information System Research (JOSH), 5(1), 53–63. https://doi.org/10.47065/josh.v5i1.4379

Iskandar Mulyana, D., & Lutfianti, N. (2023). Penerapan Sentimen Analisis Dengan Algoritma SVM Dalam Tanggapan Netizen Terhadap Berita Resesi 2023. Sisfotenika, 13(1), 53–64.

Kaka, O., Ananda Putra Tanggu Mara, A., Wulla Rato, K., Studi Teknik Informatika, P., & Tinggi Manajemen Informatika Komputer Stella Maris Sumba Email Penulis Korespondensi, S. (2023). Analisis Sentimen Dampak Perkembangan Teknologi Informasi dan Komunikasi Terhadap Kemajuan Belajar Siswa SMK Rada Pamba dengan Metode Naive Baiyes. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), 6, 191–199.

Styawati, S., Hendrastuty, N., & Isnain, A. R. (2021). Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine. Jurnal Informatika: Jurnal Pengembangan IT, 6(3), 150–155. https://doi.org/10.30591/jpit.v6i3.2870

Tineges, R., Triayudi, A., & Sholihati, I. D. (2020). Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM). Jurnal Media Informatika Budidarma, 4(3), 650. https://doi.org/10.30865/mib.v4i3.2181

Tohidi, E., Perdana Herdiansyah, R., Wahyudin, E., & Kaslani, K. (2024). Analisa Sentimen Komentar Video Youtube Di Channel Tvonenews Tentang Calon Presiden Prabowo Subianto Menggunakan Support Vector Machine. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 660–667. https://doi.org/10.36040/jati.v8i1.8560

Tri Putra, K., Amin Hariyadi, M., & Crysdian, C. (2023). Perbandingan Feature Extraction Tf-Idf Dan Bow Untuk Analisis Sentimen Berbasis Svm. Jurnal Cahaya MAndalika, 1449.

Utami, T. S. (2023). Analisis Sentimen Judul Berita Mengenai Pemindahan Ibu Kota Negara (IKN) pada Portal Berita Detik.com Menggunakan Support Vector Machine (SVM). Universitas Islam Negeri Syarif Hidayatullah Jakarta.

Wibowo, A., Firman Noor Hasan, Rika Nurhayati, & Arief Wibowo. (2022). Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier. Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa & Inovasi, 4, 239–248. https://doi.org/10.35814/asiimetrik.v4i1.3577

Winarto, W., Alwiah Musdar, I., & Hasniati, H. (2023). Analisis Sentimen Bakal Calon Presiden 2024 Menggunakan Algoritma Support Vector Machine Pada Twitter. KHARISMA Tech, 19(1), 86–98. https://doi.org/10.55645/kharismatech.v19i1.464

Yuslianson. (2023). Elon Musk Akan Ganti Logo dan Nama Twitter Jadi X, Ini Alasannya. Liputan6.Com. https://www.liputan6.com/tekno/read/5351714/elon-musk-akan-ganti-logo-dan-nama-twitter-jadi-x-ini-alasannya?page=2