Analisis Sentimen Twitter terhadap Kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat menggunakan Metode Support Vector Machine - Dhimas Wida Syahputra; Bayu Rahayudi; Lailil Muflikhah (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
COVID-19 or Coronavirus Disease-19 is a virus that infects the respiratory tract with mild to severe symptoms. The government enforces a regulation, namely the Penerapan Pembatasan Kegiatan Masyarakat (PPKM) to reduce the number of positive COVID 19. Pros and cons occur among the community. People usually express opinions on social media, such as Twitter. So that the public can express a public opinion about government policies regarding PPKM. From the public opinion on Twitter, we can analyze the sentiment. Sentiment analysis is used to determine whether a comment is negative or positive. In this case, sentiment analysis determines public comments on PPKM regulations on Twitter social media. The Support Vector Machine (SVM) algorithm is used to find out the sentiment in tweet comments. There are 500 Twitter commentary documents with a comparison of training data and data of 80% and 20%. Parameter search was conducted with the best results on the degree kernel polynomial value 3, the learning rate value 0.0001, and the Complexity 1 value. The results of the K-Fold Cross Validation test using the best parameters, namely the average accuracy of 77.2%, precision 83.3%, recall 68.7%, and F measure 75.11%.