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The geomagnetic disturbance is one of the effects of space weather which is expressed by the Dst index. The Dst index is an indication of geomagnetic field activity at middle and low latitudes. In this paper, predictions of the Dst index are carried out using an artificial neural network using the NARX method (Nonlinear Autoregressive Network with exogenous input) with the input parameters consisting of the width angle of the coronal mass ejection, the position center angle of the coronal mass ejection, the coronal mass ejection velocity, the coronal hole area, solar wind velocity, solar wind density, and interplanetary magnetic field. The data used for learning, validation, and testing with the 7 exogenous input parameters above are from 1997 to 2019. The Dst index obtained is quite good with a regression between output and target of more than 98%. This model can predict the hourly Dst index one to five days ahead. From the 8 events that occurred in 2020, we found the average RMSE error is 11.41 nT. The Dst index prediction can be used as a guide in forecasting geomagnetic disturbances in providing space weather information. [ABSTRACT FROM AUTHOR]