Forecasting the Exchange Rate of Yen to Rupiah Using the Long Short-Term Memory Method

Abstract

Long Short-Term Memory (LSTM) is a modification of the Recurrent Neural Network (RNN) to address the problems of exploding and vanishing gradients and make it possible to manage long-term information. To tackle these problems, modifications were made to the RNN by providing memory cells that can store information for long periods. This study aimed to forecast the exchange rate of  Yen to Rupiah using the LSTM method. The data used in this research is daily purchasing rate data from January 2020 to May 2023, which consists of 848 observations. The data was divided into two sets: 80% for training and 20% for testing. For the forecasting process, experiments were conducted to identify the best model by adjusting several hyperparameters. The performance of each model was evaluated using the Mean Absolute Percentage Error (MAPE). According to the experimental results, the best model was the LSTM model with a batch size of 20, 150 epochs, and 50 neurons per layer, which yielded an MAPE value of 1,5399.