ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

Abstract

This research was conducted to find information about the tendency of Indonesian people regarding the Covid-19 vaccination. The method that the author uses is by collecting data from Twitter social media using the API key provided by Twitter. The process of collecting data using a Python application with several libraries such as tweepy, pandas, numpy and nltk. After the data is crawled, then the data is cleaned with several data cleaning processes such as remove username, remove url, lower case, remove stopwords and lemmatize. Then the results are labeled with the textblob and sklearn libraries. then the data is analyzed using the Support Vector Machine (SVM) algorithm with the best comparison being 20 testing data and 80 training data or as many as 942 testing data and 3766 training data, the prediction results for testing data are f1 score 0.93, accuracy score 0.88, precision score 0.88 and a recall score of 0.99. The results showed that from 4,078 tweet data, there were 2,525 positive sentiments (43.0%), 771 negative sentiments (16.4%), and 1,912 neutral sentiments (40.6%). The results of 80% (3766) of training data and 20% (942) of test data obtained an accuracy score of 73.6%. From this study, it can be concluded that the tendency of Indonesian people when sampling data is taken is more accepting (positive responses) to government policies regarding the Covid-19 vaccination program. In the future, it is hoped that there will be a library that supports text data processing such as regional languages, because researchers found that during the data cleaning process there was a lot of word elimination, because many regional languages ​​were used by the Indonesian people in writing on social media.