Comparison of Bidirectional-LSTM and GRU Models for Sentiment Analysis in Bahasa Indonesia

Bidirectional-LSTM and GRU are models that can be used to process sequential data, including text data. This study, compares the two deep learning models to classify text for sentiment analysis using the Bahasa. The dataset used is the JKN BPJS Kesehatan mobile application user review data obtained from the Google Play Store site. After text preprocessing, the amount of data to be processed is 93517 with three target labels, positive, negative, and neutral. By using several model parameters such as Number of Units, Activation, Batch Size, Dropout, and other parameters, the test results obtained are that the Bidirectional-LSTM model has a slight accuracy value of ${9 6. 7 0 \%}$ and higher precision, recall, and F1Score values compared to the GRU model.