Forecasting Model of Gross Regional Domestic Product (GRDP) Using Backpropagation of Levenberg-Marguardt Method

The GRDP is an important indicator to measure the economic growth of a region so that the GRDP forecast future needs. This paper intends to choose a better method for forecasting the GRDP of Bandung Regency. The method used in this study is the Autoregressive Integrated Moving Average (ARIMA) time series model and the Backpropagation of Levenberg-Marquardt or Feed Forward Neural Network (FFNN) method. To measure the accuracy of forecasting carried out using Mean Absolute Percentage Error (MAPE). The GRDP data obtained from the Bandung Regency Central Bureau of Statistics, in the years 2010-2016. The results of the analysis using the ARIMA model, obtained ARIMA (0, 1, 1) model, with MAPE of 3.90%. Meanwhile, analysis using Backpropagation of Levenberg-Marquardt obtained FFNN (1, 5, 1) model, with MAPE of 3.88%. Because the MAPE value in the FFNN (1, 5, 1) model is smaller than the MAPE value in the ARIMA (0, 1, 1) model, it can conclude that the Backpropagation of Levenberg-Marquardt method better used in forecasting the GRDP of Bandung Regency.