Sentiment Analysis Related To Covid-19 Vaccination On Social Media Using The K-Nearest Neighbor (K-NN) Method - Amanda Novrianty; Mhd. Furqan; Sriani Sriani (JURNAL TEKNOLOGI DAN OPEN SOURCE, 2025)
Coronavirus 19 (COVID-19) has become a topic of great concern in the past two years. To anticipate the spread of the virus, the government has made various efforts, one of which is by procuring a COVID-19 vaccination to increase the body's immunity. In carrying out the program, the government urges the public to use social media as a means of disseminating information regarding the COVID-19 vaccination. Facebook is one of the most popular social media and is chosen by agencies as a medium of information. Information regarding the vaccination is shared by the Ministry of Health of the Republic of Indonesia through its Facebook Page and the public can provide opinions in the form of comments. Given that the comments are numerous and lengthy if you have to read the manual, it is difficult to classify which one corresponds to the positive, negative or neutral opinion class, so a system is needed to analyze them. This sentiment analysis system uses the K-Nearest Neighbor (K-NN) method to classify positive, negative and neutral opinions. This study uses 750 comments obtained from posts in November 2021 with the keywords 'vaccination' and 'vaccine', with the distribution of 700 training data and 50 test data. Furthermore, the comments are pre-processed with the stages of case folding, filtering, tokenizing, normalization, stopwords and stemming, then weighted using the TF-IDF feature. System testing is carried out using the K-Nearest Neighbor (K-NN) method with a value of k = 1, k = 3, k = 5, k = 7 and k = 9 . 1 and f-measure of 0.71428571428571. Meanwhile, the lowest accuracy value is at the value of k = 7 and k = 9 with an accuracy of 0.66 and an error rate of 0.34.