ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM)

Abstract

The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.