Abstract
This study aims to determine the accuracy of sentiment analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time using the Naive Bayes Classifier, Support Vector Machine, and K-Nearest Neighbor methods. Twitter is a social media platform that is widely used to express opinions, including those related to the COVID-19 vaccine. Sentiment analysis is a method used to classify text into positive, negative, or neutral sentiments. The data used in this study were taken from the Twitter of the DKI Jakarta Provincial Government from January to March 2021. The results of the study show that the Support Vector Machine method has the highest accuracy of 85%, followed by Naive Bayes Classifier with an accuracy of 82%, and K-Nearest Neighbor with an accuracy of 78%. These results indicate that the Support Vector Machine method is the most suitable for sentiment analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time.
Keywords
Sentiment Analysis, Twitter, COVID-19 Vaccine, Naive Bayes Classifier, Support Vector Machine, K-Nearest Neighbor
Full Text
This study aims to determine the accuracy of sentiment analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time using the Naive Bayes Classifier, Support Vector Machine, and K-Nearest Neighbor methods. Twitter is a social media platform that is widely used to express opinions, including those related to the COVID-19 vaccine. Sentiment analysis is a method used to classify text into positive, negative, or neutral sentiments. The data used in this study were taken from the Twitter of the DKI Jakarta Provincial Government from January to March 2021. The results of the study show that the Support Vector Machine method has the highest accuracy of 85%, followed by Naive Bayes Classifier with an accuracy of 82%, and K-Nearest Neighbor with an accuracy of 78%. These results indicate that the Support Vector Machine method is the most suitable for sentiment analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time.