SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY ON COVID-19 VACCINATION ON TWITTER SOCIAL MEDIA

  • Nurmalasari Nurmalasari (1*) STMIK Nusa Mandiri
  • Widi Astuti (2) Universitas Nusa Mandiri
  • Windu Gata (3) Universitas Nusa Mandiri
  • Ida Zuniarti (4) Universitas Nusa Mandiri

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v18i2.3820

Keywords: Data Mining, Covid-19 Vaccine, Twitter, Naive Bayes, SVM, Logistic Regression

Abstract

In the process, data mining will extract valuable information by analyzing the existence of specific patterns or relationships from extensive data. One of the concerns of the new disease outbreak caused by the coronavirus (2019-nCoV) or commonly referred to as Covid-19, was officially designated as a global pandemic by the World Health Organization (WFO) on March 11, 2020. To break the transmission of Covid-19, the government carried out vaccinations for the Indonesian population. In the first period, the vaccination target will be for health workers with a total of 1.3 million people, public officers with 17.4 million people, and 21.5 million people. 19. The Data processed is only text data from Twitter application reviews that use Indonesian. Using the polarity of the Sentiment class Textblob, the sentiment class is positive, negative, and neutral. The data mining used is SVM, Naive Bayes, and Logistic Regression. As for this research in the form of knowledge of sentiment in the community towards vaccination activities, the results of this study get 43% positive sentiment, 40.8% negative, and 16.2% negative by testing the classification algorithm, Logistic Regression accuracy of 87%, SVM 86, 4%, and Naive Bayes, 40% of these results, can be seen that the Indonesian people have a positive sentiment towards the covid-19 vaccine.

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Published
2022-09-13
How to Cite
Nurmalasari, N., Astuti, W., Gata, W., & Zuniarti, I. (2022). SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY ON COVID-19 VACCINATION ON TWITTER SOCIAL MEDIA. Jurnal Pilar Nusa Mandiri, 18(2), 161-166. https://doi.org/10.33480/pilar.v18i2.3820
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