Artificial neural networks (ANNs) have been extensively studied and have been used as time series forecasting method. When neural network is compared to seasonal ARIMA (SARIMA) model, SARIMA model outperforms neural networks model when seasonality in a series exist. This paper aims to investigate the effectiveness of preprocessing data in neural networks model. In this study, the trend is absent and only seasonality exists, hence we only applied deseasonalization as preprocessing data. The forecasting performances among these three models, i.e., the SARIMA model, the neural network model with raw data and the neural network models with preprocessing data are compared. Comparing the performances using the root mean squared error (RMSE), the mean absolute error (MAE) and mean absolute percentage error (MAPE), we find that neural networks with preprocessing data are able to capture seasonality but SARIMA still outperforms both two neural network models.