Analisis Sentimen Opini Publik Tentang Vaksin Booster Menggunakan Metode Support Vector Machine dan firefly Algorithm
Abstract
Coronavirus is a viral epidemic that has swept across the world. Indonesia is one of the countries affected by the virus, so the government is trying to prevent the spread of the coronavirus so that not many people are exposed to the coronavirus. One of the government's efforts to prevent the spread of this virus is to create a free vaccination program. In January 2022, the government issued the latest vaccine, namely the booster vaccine. The emergence of the booster vaccine is slightly doubted by the public because of the many hoax news about this booster vaccine. This study aims to analyze the sentiment of public opinion on the emergence of the booster vaccine. The data used for this analysis comes from public opinion on Twitter. The data is taken using the crawling method. This research uses the support vector machine (SVM) method as a process for classifying public opinion and the firefly algorithm as an optimization of SVM parameters. There are 3 class labels used for classifiers, namely positive, negative, and neutral. A lot of data used after the pre-processing process is 2223 data which is then split into as much as 80% training data and 20% testing before entering the classification stage using the SVM method. The results of the classification using SVM produce an accuracy of 85% on the default parameters and after being optimized using the firefly algorithm it produces an accuracy of 86% with parameters C = 1.0–3.0, = 0.1-1.0.
References
Anjasmoros, M. T., Istiadi, I., & Marisa, F. (2020). Analisis sentimen aplikasi go-jek menggunakan metode SVM dan NBC (Studi kasus: Komentar pada play store). In Conference on Innovation and Application of Science and Technology (CIASTECH), 3(1), pp. 489-498). Retrieved from http://publishing-widyagama.ac.id/ejournal-v2/index.php/ciastech/article/view/1905.
Giovani, A.P., Ardiansyah., Haryanti, T., Kurniawati, L., Gata, W. (2020). Analisis sentimen aplikasi ruang guru di twitter menggunakan algoritma klasifikasi. Jurnal TEKNOINFO, 14(2), 116-124. https://doi.org/10.33365/jti.v14i2.679.
Husada, H. C., & Paramita, A. S. (2021). Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM). Teknika, 10(1), 18-26.
Isnain, A. R., Sakti, A. I., Alita, D., & Marga, N. S. (2021). Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm. Jurnal Data Mining Dan Sistem Informasi, 2(1), 31-37.
Karyono., Rohadin., & Indriyani, D. (2020). Penanganan dan pencegahan pandemi wabah virus corona (COVID-19) kabupaten Indramayu. Jurnal Kolaborasi Resolusi Konflik, 2, 164-173. doi.org/10.24198/jkrk.v2i2.29127.
Laurensz, B., & Eko Sediyono. (2021). Analysis of public sentiment on vaccination in efforts to overcome the covid-19 pandemic. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 10(2), 118-123. https://doi.org/10.22146/jnteti.v10i2.1421.
Novantika, A., & Sugiman, S. (2022, February). Analisis Sentimen Ulasan Pengguna Aplikasi Video Conference Google Meet menggunakan Metode SVM dan Logistic Regression. In PRISMA, Prosiding Seminar Nasional Matematika (Vol. 5, pp. 808-813).
Pamungkas, F. S., & Kharisudin, I. (2021, February). Analisis Sentimen dengan SVM, NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter. In PRISMA, Prosiding Seminar Nasional Matematika (Vol. 4, pp. 628-634).
Sasongko, T. B. (2016). Komparasi dan Analisis Kinerja Model Algoritma SVM dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA). Jurnal Teknik Informatika dan Sistem Informasi, 2(2).
Zulaikha, L. I. (2021). Pemberian Vaksin Sinovac Kepada Masyarakat Untuk Mencegah Penyebaran Covid 19. Jurnal Pengabdian Kepada Masyarakat, 2(2), 34-37.
