Sentiment Analysis Pemutusan Hubungan Kerja Akibat Pandemi Covid-19 Menggunakan Algoritma NaïveBayes Dan PSO - Wiyanto Wiyanto; Zulita Setyaningsih (Jurnal Sisfokom (Sistem Informasi dan Komputer), 2021)
The Pandemic Covid-19  in Indonesia in 2020 had an impact on Termination of Employment (PHK), this has received various public opinions on social media. At a time when the poverty rate is high and unemployment increases every year, it becomes a factor of public disapproval of Termination of Employment (PHK). It is necessary to classify public opinion into a negative opinion or a positive opinion on this issue. The purpose of this study is to analyze the sentiment towards layoffs to determine negative or positive opinions using the Naïve Bayes algorithm by adding feature selection. The research stages consist of data collection, text preprocessing, feature selection, and application of algorithms. The testing process in this study uses the Rapid Miner application. The test results in this study using the Naive Bayes Algorithm, the accuracy value is 93.57% and for addition to the Naïve Bayes + PSO feature selection, the accuracy value is 93.71%. The best accuracy value in sentiment analysis of layoffs in the covid-19 pandemic is the addition of the PSO feature selection in the Naïve Bayes Algorithm, which is 0.14% better.