Performance Comparison Between Artificial Neural Network, Recurrent Neural Network and Long Short-Term Memory for Prediction of Extreme Climate Change

Abstract

Extreme climate change is the most common problem in Indonesia. Extreme climate change for months can cause various natural disasters. Therefore, it is necessary to make predictions about climate change that will occur in order to avoid the risk of future conflicts. This study uses the Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) algorithms by comparing the performance of the three using Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) evaluations. The results of this study indicate that RNN is better at predicting temperature in Indonesia compared to ANN and LSTM. This is evidenced by the MAPE value generated by the RNN which is smaller than the ANN and LSTM, which is 1.852 %, the RMSE value is 1,870, and the MSE value is 3,497.