Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB

• Main Authors: Muhammad Rivza Adrian, Muhammad Papuandivitama Putra, Muhammad Hilman Rafialdy, Nur Aini Rakhmawati
• Format: Article
• Language: Indonesian
• Published: Universitas PGRI Semarang 2021-06-01
• Series: Jurnal informatika UPGRIS
• Subjects: sentiment analysis; covid-19; psbb; support vector machine; random forest
• Online Access: http://journal.upgris.ac.id/index.php/JIU/article/view/7099
• ISSN: 2460-4801; 2477-6645

COVID-19 in Indonesia, has made the local government not remain silent. Several local governments in Indonesia have enacted regulations to reduce the growth of COVID-19 victims by limiting public meetings with Large-Scale Social Restrictions or LSSR. However, the implementation of this LSSR has received many comments from social media users, especially from Twitter. This research was conducted with the aim of analyzing the sentiment of implementing the LSSR with media tweets on the Twitter social media platform. The data that were successfully extracted were 466 tweet data with training data and test data having a ratio of 7 to 3. Then the data was calculated into 2 different algorithms to be compared, the first algorithm used was the Support Vector Machine (SVM) algorithm and Random Forest with the aim get the most accurate sentiment analysis results.