ABSTRACT
In Indonesia, the implementation of the national COVID-19 (Coronavirus disease of 2019) vaccination programmes has received criticism from various strata of society, especially through social media platforms such as Twitter. Therefore, Twitter can be used as a data source to analyze Indonesian public sentiment regarding the vaccination programme. Various classical machine learning methods exist for sentiment analysis, but these methods require complex feature engineering and do not focus on the importance of word order in a sentence. In this study, a deep learning model, bidirectional encoder representation from transformer (BERT), is used to overcome these problems by conducting experiments to determine the best dataset after pre-processing, the best hyper-parameter, and the best pre-trained model for BERT. The data used in this study were Indonesian Twitter data with a total of 3000 tweets. Our results demonstrate that BERT is suitable for performing sentiment analysis. In our experiments, BERT obtained better results than classical machine learning methods, with a precision of 86.2%, recall of 86%, f1-score of 86%, and accuracy of 86%. The results of the sentiment analysis performed in this study can be considered by the government in formulating policies related to the implementation of vaccination programmes. ICIC International ©2023.