Comparison between the backpropagation and single exponential smoothing method in sugar production forecasting case

Abstract

Sugar is a strategic commodity, because it is one of the basic needs consumed by the community. The decline in sugar production causes turmoil both economically and politically. So companies must be able to balance sugar production in accordance with market demand. Because, if the sugar production exceeds market demand, it will cause accumulation and expenditure costs that exceed the limit. Conversely, if the sugar production is too little, then the company is unable to meet demand. This study was comparing the Single Exponential Smoothing method and the Backpropagation method in predicting the amount of sugar production. The data used was based on production in 2013-2017, with predictions every month starting in the fifth month. The alpha parameter in Single Exponential Smoothing was 0.84, while in Backpropagation used 5 input nodes and 5 hidden layer nodes. From the results of tests that have been carried out, it can be seen that the Backpropagation method produces the smallest MSE and MAPE, amounting to 57187817.49 and MAPE of 1.012% compared to the Single Exponential Smoothing method with MSE of 83602989.43 and MAPE of 1.46%.