Forecasting Product Sales of Crackers Using Artificial Neural Network Method and Double Exponential Smoothing Holts
Peramalan Penjualan Produk Kerupuk Pasir Menggunakan Metode Artificial Neural Network dan Double Exponential Smoothing Holts
DOI:
https://doi.org/10.21070/ups.2366Keywords:
Prediction, Artificial Neural Network, Double Exponential Smoothing Holts, Sand Cracker ManufactureAbstract
UD. XYZ is a producer of sand crackers with fluctuating sales levels the highest fluctuating value of 37% in March 2020. As a result, there was a buildup of 4197 kg raw materials for four years and a shortage of raw materials in August 2021. This research aims to assist companies in predicting sales in the coming period. by proposing the best sales forecasting method as an improvement in anticipating raw material control. The method used in this research is an artificial neural network and double exponential smoothing holts. The results showed that the accuracy of sales forecasting on the artificial neural network has the best accuracy rate of 0.118 compared to double exponential smoothing holts of 11.639. The conclusion the research is proposing improvements to sales prediction methods to anticipate declining sales the next period as well as improvements to raw material ordering planning experience extreme excesses and shortages.
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