Intermittent demand data is one of the data types with a very random pattern, for example demand data. The data will have value (not zero) if there is a demand. If there is no demand, the data is zero. Intermittent demand data is usually called customer demand data or sales data for an item that is not sold every time. The general problem is that demand is not always continuous but intermittent. This natural fact makes intermittent data not easy to predict. Standard methods used to predict intermittent demand data are Croston. Single exponential smoothing (SES) is also commonly used in practice. The Croston and exponential smoothing generally produce static forecasts. This study proposes a deep learning method i.e. recurrent neural network (RNN) and deep neural network (DNN) to predict intermittent data. The simulation study was carried out by generating data with 72 different design parameters and doing 50 repetitions. Also, the empirical study uses M5 competition data from the Kaggle website. This study aims to measure the performance of RNN and DNN, compared to Croston and SES as the benchmark methods, in predicting intermittent demand data. The performance measurement uses mean absolute error (MAE) and root mean squared scaled error (RMSSE). Deep learning performance in simulation studies successfully outperformed Croston and SES in several design parameters. In empirical studies, only the RNN method outperformed the benchmark methods. This study also found other information that the measurement of MAE is more robust than RMSSE.