Sentimen Analisis Opini Masyarakat Terhadap Kebijakan Kominfo atas Pemblokiran Situs non-PSE pada Media Sosial Twitter
DOI:
https://doi.org/10.30865/jurikom.v9i5.4950Keywords:
Sentiment Analysis, Classification Method, KNN, Random Forest, TwitterAbstract
Kominfo is a state government agency in the field of communication and information. Some time ago Kominfo issued a policy, namely the blocking of sites that do not register themselves as PSE. This policy has become a hot topic on social media, especially Twitter. Therefore, this research was conducted by collecting public opinion, especially on Twitter for sentiment analysis. The goal is to be able to classify public responses into positive, negative and neutral opinions using the InSet Lexicon. In addition, the classification will compare several classification methods, namely Decision Tree, KNN, Naïve Bayes, Random Forest, Logistic Regression and SVM. The results of this study stated that as many as 1234 tweets that had been pre-processed tended to be negative with a percentage of 82.82%. while positive tweets are worth 10.53% and neutral tweets are 6.65%. The method with high accuracy value in this study is the KNN and Random Forest methods with an accuracy of 85.8%.
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