DOI: https://doi.org/10.14710/medstat.18.1.1-12

COMPARATIVE EVALUATION OF ARIMA AND GRU MODELS IN PREDICTING RUPIAH DOLLAR EXCHANGE RATE

*Dwi Fitrianti  -  Department of Statistics and Data Sains, IPB University, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat, Indonesia 16680, Indonesia
Ratu Risha Ulfia  -  Department of Statistics and Data Sains, IPB University, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat, Indonesia 16680, Indonesia
Khairil Anwar Notodiputro  -  Department of Statistics and Data Sains, IPB University, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat, Indonesia 16680, Indonesia
Yenni Angraini  -  Department of Statistics and Data Sains, IPB University, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat, Indonesia 16680, Indonesia
Laily Nissa Atul Mualifah  -  Department of Statistics and Data Sains, IPB University, Jl. Raya Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat, Indonesia 16680, Indonesia
Open Access Copyright (c) 2025 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
This study evaluates the effectiveness of the ARIMA (Autoregressive Integrated Moving Average) and GRU (Gated Recurrent Unit) models in forecasting the USD–Rupiah exchange rate. Exchange rate fluctuations influence overall economic stability, making accurate forecasting crucial. Monthly data from January 2001 to March 2024 were analyzed. The ARIMA model, a traditional statistical approach, combines autoregressive (AR), differencing (I), and moving average (MA) components to capture linear patterns, while the GRU model, a deep learning approach, captures nonlinear and complex temporal relationships using update and reset gate mechanisms to retain long-term information. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The GRU model achieved a MAPE of 1.74%, lower than the ARIMA model’s 1.94%, and generated a forecast of Rp. 16,399.91 for April 2024, closer to the actual value of Rp. 16,249.00 compared to ARIMA’s Rp. 15,857.68. The findings demonstrate the GRU model’s superior forecasting performance and provide empirical evidence of its effectiveness in modeling volatile exchange rate data, particularly the Rupiah–USD rate.
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Keywords: ARIMA; GRU; Forecasting; Exchange rate

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Language : EN
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