Sentiment Analysis of Public Opinion Covid-19 Vaccine Using Naïve Bayes and Random Forest Methods

Ines Sholekha, Ahmad Faqih, Agus Bahtiar


The emergence of COVID-19 or 2019 coronavirus disease has been reported as a problem with a new type of disease caused by SARS-Voc 2. It has spread to 223 countries and 25 areas around the world, including Indonesia. COVID-19 has deeply affected many aspects of our lives, the environment, mental health and the economy. Twitter is one of the media outlets that is busy discussing news regarding the COVID-19 vaccine. Covid-19 has been a major impact. The Government has implemented policies such as large-scale social restrictions to address the spread of COVID-19. The elevated spread of COVID-19 has prompted the Government of Indonesia to encourage the production of a COVID-19 vaccine. The provision of the COVID-19 vaccine has become a boon and a boon to the people of Indonesia. A lot of people don't want to be vaccinated because the news of the impact of vaccination is spreading on social media, even if the news isn't necessarily real. The Government is looking for ways to continue vaccinating the community, including by collaborating with community leaders, influencers and others. The purpose of this study is to identify the community response to the vaccine so that the right strategy can be used. The results of this study yielded 89.79% for Naïve Bayes and 84.62% for Random Forest. Indonesians are giving positive responses to the administration of the COVID-19 vaccine.


COVID-19; Sentiment; Twitter; Vaccine; Classification

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A. Harun and D. P. Ananda, “Analysis of Public Opinion Sentiment About Covid-19 Vaccination in Indonesia Using Naïve Bayes and Decission Tree Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve Bayes dan Decission Tree,” Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. April, pp. 58–63, 2021.

M. Lestandy, A. Abdurrahim, and L. Syafa’ah, “Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes,” J. Resti, vol. 5, no. 10, pp. 802–808, 2021.

C. Villavicencio, J. J. Macrohon, X. A. Inbaraj, J. H. Jeng, and J. G. Hsieh, “Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes,” Inf., vol. 12, no. 5, 2021, doi: 10.3390/info12050204.

W. Yulita, E. D. Nugroho, and M. H. Algifari, “Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier,” Ejurnal.Teknokrat.Ac.Id, vol. 2, no. 2, pp. 1–9, 2021, [Online]. Available:

M. Syarifuddin, “Analisis Sentimen Opini Publik Terhadap Efek Psbb Pada Twitter Dengan Algoritma Decision Tree-Knn-Naïve Bayes,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 87–94, 2020, doi: 10.33480/inti.v15i1.1433.

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” Procedia Comput. Sci., vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.

S. N. J. Fitriyyah, N. Safriadi, and E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, p. 279, 2019, doi: 10.26418/jp.v5i3.34368.

F. F. Abdulloh and I. R. Pambudi, “ANALISIS SENTIMEN PENGGUNA YOUTUBE TERHADAP PROGRAM VAKSIN COVID-19,” CSRID J., vol. 13, pp. 141–148, 2021.

M. S. Alrajak, I. Ernawati, and I. Nurlaili, “Analisis Sentimen Terhadap Pelayanan PT PLN di Jakarta pada Twitter dengan Algoritma K- Nearest Neighbor (K-NN),” Semin. Nas. Mhs. Ilmu Komput. dan Apl., pp. 110–122, 2020.

Pristiyono, M. Ritonga, M. A. Al Ihsan, A. Anjar, and F. H. Rambe, “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012045, 2021, doi: 10.1088/1757-899x/1088/1/012045.

M. A. Fauzi, “Random forest approach fo sentiment analysis in Indonesian language,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 1, pp. 46–50, 2018, doi: 10.11591/ijeecs.v12.i1.pp46-50.

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