Daily USD-IDR exchange rate prediction using Long Short-Term Memory (LSTM) with macroeconomic indicators and recursive multi-step prediction

Abstract

The foreign exchange market’s high volatility and non-linear dynamics pose significant challenges for accurate currency price forecasting. This study develops a predictive model for the daily USD-IDR exchange rate using Long Short-Term Memory (LSTM) method integrated with macroeconomic indicators. Historical price data, global oil prices, interest rates, and the US Dollar Index (DXY) were collected by fetching from Yahoo Finance and FRED. A comprehensive experimental design comprising 432 configurations was evaluated across varying data periods, train-validation-test splits, feature combinations, and hyperparameters. Results indicate that a 10-year historical dataset yields the most stable and accurate performance, achieving a test MAPE of 0.52% and R² of 0.904. The integration of macroeconomic variables improved predictive capability, though the impact of DXY was found to be conditional on data split ratios and hyperparameter settings. The optimal model was deployed as an interactive web application using Streamlit, enabling users to forecast USD-IDR rates up to seven days ahead via recursive multi-step forecasting. The proposed system provides a practical, accessible tool for retail traders and financial analysts to support informed decision-making in volatile forex markets