Text Classification for Analysing Indonesian People's Opinion Sentiment for Covid-19 Vaccination - eka miranda; Veronica Gabriella; Sriyanda Afrida Wahyudi; Jennifer Chai (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2023)
The purpose of this study is to implement text mining for sentiment analysis of Indonesian public opinion on COVID-19 vaccination on Twitter social media using text classification techniques Support Vector Machine (SVM) and Random Forest. The research begins with crawling data from Twitter from September 2021 to October 2021; data cleansing; text translation into English; data preprocessing using NTLK performed with and without the lemmatization process; sentiment analysis using TextBlob; distribution of training and testing data with the Hold-Out method of 70:30 and 80:20; hyperparameter tuning with GridSearchCV; text classification with SVM and Random Forest; and testing the classification results by calculating Accuracy, Precision, Recall, F-Measure based on confusion matrix. The results show that text classification Random Forest consistently has a higher accuracy rate than SVM with the highest accuracy value of 90,59% and most of the sentiments indicate neutral to the COVID-19 vaccination program.