Abstract

Currency exchange rate is an exchange between two different currencies, which is a comparison of the value or price between the two currencies and this comparison is often called the exchange rate. Currency exchange rate movements are very complex and influenced by many factors, including economic, political, and social factors. In an effort to understand and predict these movements, many studies have been conducted using various methods of analysis and prediction. however, there is still no consensus on the best method to predict exchange rate movements. This study aims to compare the performance between the Long Short Term-Memory and Random Forest algorithms in predicting the exchange rate of the Rupiah (IDR) against the Singapore Dollar (SGD). By utilizing the historical data of currency exchange rate movements, the main data and the data of import and export values from the two countries as additional variables, After going through a series of stages ranging from data collection, preprocessing, to modeling, the evaluation results show that the Long Short Term-Memory algorithm has a better performance with a Root Mean Square Error (RMSE) of 152.28, Mean Absolute Percentage Error (MAPE) 1.25%, and 98.74% accuracy, while Random Forest has an RMSE of 284.3, a MAPE of 2.07%, and an accuracy of 97.93%. These results show that Long Short Term-Memory is superior in capturing complex exchange rate change patterns, making it a more effective choice in predicting currency exchange rates than Random Forest.

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