Analisis Sentimen Tweet Penanganan Covid-19 di Indonesia Menggunakan SVM dan Naïve Bayes dengan Operator Seleksi Fitur Information Gain - Hasna, Aisyah Nur; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul (Jurnal Ilmiah Wahana Pendidikan, 2024)
Opinion that is present from the public is one indicator of sentiment assessment that can be used to assess a matter. In 2020, the world is experiencing a COVID-19 pandemic so that Indonesia is also affected. On Twitter social media at that time there was a lot of discussion about the virus and the state of government policy at that time. Through these tweets, there are those who agree to provide a response to the policy, there are also those who oppose or disagree. Producing these responses is divided into two types of groups, namely positive and negative groups. In this study, tweets were analyzed using two algorithms, namely SVM and Naïve Bayes compared with and without feature selection by the information gain operator so that information is extracted that public opinion tends to be positive or negative. Comparing the algorithms in this study resulted in the highest level of accuracy using the SVM method plus information gain which resulted in an accuracy rate of 66.7% with a precision of 65.5%, a recall value of 66.9% and an f1-score of 66.2%.