K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia

Abstract

In order to prevent the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has been implementing a booster vaccine program since January 12th, 2022, with priority for the elderly and vulnerable groups as well as those who got the second C-19 vaccine longer than 6 months. The implementation of this program raised many pros and cons among public which were expressed either positively or negatively through social media. Therefore, sentiment analysis is needed to examine these phenomenons. This study aims to determine the positive and negative response from public by employing K-Nearest Neighbor method. A total of 2,000 commentary data were collected to be in turn classified based on positive and negative sentiments. There are 500 comments used as training data and divided equally to positive and negative class, each consists of 250 data. Using the value of K = 9, the results show a positive sentiment of 43% while a negative sentiment of 57%. Based on the validity test using 10-fold cross validation, an accuracy of 82.60% was obtained, a recall value was 82.60% with a precision of 83.89%.

Keywords

• k-nearest neighbors
• rapidminer
• sentiment analysis
• vaccine booster