ANALISIS SENTIMEN PADA TWITTER TENTANG ISU PERILAKU ANTISOSIAL DENGAN ALGORITMA NAÏVE BAYES

Abstract

In 2023, around 78.19% of the 275.77% or 215.63 million Indonesian population will 
be connected to the internet, with positive impacts such as fast communication, 
entertainment and new knowledge. The internet makes non-cash transactions easier and 
has negative impacts such as addiction and antisocial behavior such as indifference to 
people around you. Teenagers often access social media, especially Twitter, to express 
opinions and vent both positive and negative. Sentiment analysis is used to determine 
opinions about antisocial behavior on Twitter by using text mining techniques to analyze 
teenagers' opinions. Naive Bayes and SVM algorithms are used in sentiment analysis on 
the Twitter dataset to analyze antisocial behavior. Actions to evaluate the Naive Bayes 
algorithm in assessing antisocial behavior sentiments had the best accuracy results of 
59.71% with k=7 without n-grams. The Naïve Bayes algorithm with k=5 and n-gram n=2 
has the best precision of 33.76% and the best recall of 33.45%. Future research can try to 
use other classification algorithms such as KNN, SVM, etc. To find the best accuracy of the 
antisocial behavior dataset.