Analisis Sentimen Di Media Sosial Twitter Dengan Studi Kasus Vaksinasi Covid-19

  • Nufia Universitas Jenderal Achmad Yani Yogyakarta
  • Aris Wahyu Murdiyanto
  • Kharisma
Keywords: sentiment analysis, Naïve bayes classifier, Anti covid-19 vaccines


With the COVID-19 pandemic, the World Health Organization or WHO conducted research and research trials on the COVID-19 vaccine. The Indonesian government has made several policies, one of which is the "Mass Vaccination Program". However, the COVID-19 vaccination program in the field received mixed responses in the community, there were those who supported the vaccine program and some who rejected the vaccine program. In this study, researchers conducted research on sentiment analysis on the opinion of vaccination programs against anti-vaccine community groups based on Twitter social media data using the Naïve Bayes Classifier algorithm to provide information on opinion assessments that lead to positive and negative sentiments.

Objective: The purpose of this study is to find out the public perception of AntiVaccine against the COVID-19 Vaccination Program in Indonesia.

This study uses the Naïve Bayes Classification. The use of the Naïve Bayes Classifier (NBC).

This research uses tweets obtained from Twitter with the keywords/hashtags “Anti Covid-19 Vaccines” or by collecting data based on accounts related to news about vaccination programs such as @ The Ministry of Health of the Republic of Indonesia. Data collection was carried out in the period August 2021-December 2021, with a total of 889 data. This study has succeeded in obtaining an accuracy of 72 % for testing.  The result of the final sentiment analysis in the classification of the Anti-Vaccine group in this study is "Negative Sentimen".