ANALISIS SENTIMEN TERHADAP PPKM DARURAT PADA MEDIA SOSIAL TWITTER MENGGUNAKAN METODE NAÏVE BAYES DENGAN SELEKSI FITUR INFORMATION GAIN - Albet Dwi Pangestu (2022)
Twitter is a social media used by the public as a medium for communicating and expressing opinions. Since the COVID-19 pandemic hit Indonesia, the government has issued many policies to suppress the spread of COVID-19, one of which is PPKM Darurat. Many public opinions criticize or support this policy on social media, especially Twitter. This study aims to build a sentiment analysis model for PPKM Darurat on Twitter social media with the hashtag #ppkmdarurat. In this study, the Naïve Bayes method and the Information Gain method will be used as feature selection. This study will compare the use of Information Gain and not use Information Gain as a feature selection. Data collection will be crawled using the R programming language and integrated into the API provided by Twitter. After filtering the data, it became 770 labeled 335 positive and 335 negative. The results of testing the Naïve Bayes classification model showed an increase in performance when using the Information Gain feature selection with a top ranking value of '> 0.0001', namely accuracy 0.81, recall 0.82, precision 0.84, f1 score 0.83 and specificity 0.79 compared to the previous one, namely accuracy 0.79, recall 0.81, precision 0.81, f1 score 0.81 and specificity 0.76.