SENTIMENT ANALYSIS OF THE COVID-19 BOOSTER VACCINATION PROGRAM AS A REQUIREMENT FOR HOMECOMING DURING EID FITR IN INDONESIA

Angga Pratama, Raksaka Indra Alhaqq, Yova Ruldeviyani

Research output: Contribution to journalArticlepeer-review

Abstract

The COVID-19 vaccination program was carried out to overcome the pandemic. In addition to vaccination, there is a booster vaccine program which is also an obligation for the public to be followed but has not received a good response from the public. Indonesia's government is making booster vaccination a mandatory requirement for mass homecoming in the Islamic celebration day of Eid Fitr, known as mudik Lebaran. This study aims to find out public opinion and perception regarding the booster vaccination program for mudik Lebaran using sentiment analysis. This study uses eight classification modeling: Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, Random Forest, K-Nearest Neighbor, AdaBoost, and XGBoost. The best classification modeling is SVM with the best accuracy score 88% and the F1 score 88%. Then this SVM model is used to predict the sentiment of 30,582 tweet data from March 22 to May 02, 2022. The results are 11,507 giving negative sentiment (37.63%) and 19,075 giving positive sentiment (62.37%). This result shows that the government's strategy in accelerating the COVID-19 booster vaccination program was well accepted by making it a requirement for mudik Lebaran. Furthers analysis with visualization of time series, shows that the sentiment had eveloved. In the first week, negative sentiment prevailed due to reactions to this policy. The Indonesian people compare the policy of the MotoGP event in Mandalika which does not require a vaccine booster. After that, in the following weeks the positive sentiment prevailed because the community realized that boosters were important to maintain their health and that of their families back home. This research shows the importance of time series visualization because sentiment can change over time.

Original languageEnglish
Pages (from-to)248-261
Number of pages14
JournalJournal of Theoretical and Applied Information Technology
Volume101
Issue number1
Publication statusPublished - 15 Jan 2023

Keywords

  • Classification
  • COVID-19
  • Mudik Lebaran
  • Sentiment Analysis
  • Time Series
  • Vaccine Booster

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