Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM
Rima Tamara Aldisa, Pandu Maulana
Abstract
The impact of the COVID-19 pandemic is very broad, especially in Indonesia. In early 2022, Indonesia entered the early stages of recovering conditions caused by the pandemic. The government has an option for the community to carry out a third dose of vaccination (booster). However, there are a number of pros and cons in the community regarding the booster vaccine. This study aims to conduct sentiment analysis related to public opinion on the COVID-19 booster vaccination in Indonesia with Naive Bayes model. The data source used comes from Twitter. The workflow of this research includes data crawling, labeling, preprocessing, dataset sharing, and model testing and comparison with other models, namely Decision Tree and SVM. The results of this study indicate that the largest AUC score falls to the SVM model (75.40%), but for more accurate precision falls to the Naive Bayes model (83.81%). In addition, there is a confusion matrix which shows that the Naive Bayes model trial is running well.