<![CDATA[The global economic recession predicted for 2023 poses significant risks to Indonesia, particularly regarding the weakening of the national currency against foreign currencies. Forecasting serves as a crucial systematic approach to predict future exchange rate fluctuations based on historical data. This study aims to forecast the Rupiah exchange rate against the Euro using Artificial Intelligence approaches: Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Both methods are variants of Recurrent Neural Networks (RNN) capable of learning long-term dependencies through memory cells, with GRU offering a simpler and more efficient architecture than LSTM. This research compares the performance of LSTM and GRU models to determine the most accurate method for predicting the exchange rate. The findings are expected to identify the optimal forecasting model and provide valuable information for anticipating currency changes amidst economic instability.]]>
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