The construction of the circuit is one of the policies made by the Indonesian government to advance the tourism sector and improve the national economy. This policy triggers various opinions given by the public, primarily through social media Twitter, both in the form of positive and negative opinions. This study compares machine learning and deep learning algorithms, Support Vector Machine and IndoBERTweet, that will be used as a model to predict the sentiment of racing circuit construction tweets. These models are built with K-Fold cross-validation to obtain the overall model’s performance for the entire dataset. Based on the experiments that have been carried out, it shows that IndoBERTweet performs better than the Support Vector Machine, with an overall accuracy score of 86%, a precision score of 88.2%, a recall score of 88.6%, and an f1-score of 88.4% for the entire dataset. Meanwhile, the Support Vector Machine model only achieves 82% for the accuracy score, 87.3% for the precision score, 84.3% for the recall score, and 85.8% for the f1-score. In addition, the best accuracy value from each iteration for IndoBERTweet is 94%, and the Support Vector Machine is 93%.
Keywords: IndoBERTweet; K-Fold Cross Validation; Racing Circuit; Sentiment Analysis; Support Vector Machine.