Comparison of K-Nearest Neighbor and Support Vector Machine for Sentiment Analysis of the Second COVID-19 Booster Vaccination (Universitas Brawijaya, 2024)
K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are classification methods commonly used in sentiment analysis. Sentiment analysis can be applied to analyze the opinion of social media specially on platforms like Twitter about a specific topic. The purpose of this research is to determine and compare the classification accuracy of the K-NN and SVM One Against One algorithm in classifying the sentiments of the public regarding the implementation of the second Covid-19 booster vaccination on Twitter. The data used in this research consists of tweet from July 29th 2022 to February 15th 2023 that contain aspects of adverse events following immunization and perception of vaccination effectiveness. The result of this study obtained that there were 1,576 tweets containing positive sentiments, 169 containing neutral sentiments, and 424 containing negative sentiments. The data is divided into training data and testing data with a ratio of 80%:20%. The classification accuracy obtained for K-NN with k=17 is accuracy of 85.48%, precision of 78.64%, and recall of 85.48%, while for SVM, the accuracy is 84.33%, precision of 78.34%, and recall of 84.33%. Based on the classification obtained by K-NN with k=17, it is better at classifying sentiment regarding the second Covid-19 booster vaccination. Keywords: K-Nearest Neighbor, Sentiment Analysis, Support Vector Machine, The Second Covid-19 Booster Vaccination