PENERAPAN METODE LSTM (LONG SHORT TERM MEMORY) DALAM ANALISIS SENTIMEN TERHADAP PELAKSANAAN PILKADA DI MASA PANDEMI COVID-19 - Melati, Dika; Herfina, Herfina; Mulyati, Mulyati (J-Icon : Jurnal Komputer dan Informatika, 2024)
The implementation of regional elections in the midst of the Coronavirus disease 2019 (COVID-19) pandemic has become the spotlight and discussion of the wider community. This is because it involves many people so that it becomes one of the risks of transmission of the virus. Therefore, this study analyzes how public sentiment towards the elections amid the COVID-19 Pandemic. One method used in sentiment analysis is Long Short-Term Memory (LSTM). LSTM is a development of the Recurrent Neural Network (RNN) model that can replace problematic RNN nodes in the hidden layer with LSTM cells designed to store previous information. The data used in this research is Indonesian tweet data taken from Twitter with a total of 5053 data classified into positive and negative sentiment classes. The data from the tweets is preprocessed first to make it easy to classify, then selected and transformed to be processed in data mining using LSTM. The LSTM classification was evaluated using the Confusion matrix to obtain an accuracy of 79,78%, a precision of 69% and a recall of 64%. Tests were also carried out with different epochs and dropouts, it was found that the average accuracy value was more than 80%, and the greatest accuracy was obtained with epoch of 45 and dropout of 20.