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Implementation of Artificial Neural Network for Product Sales Prediction at Rice Milling Unit (RMU)
Elvika Rahmi (a), Denny Jean Cross Sihombing (b*), Alexander Wirapraja (b), Putri Taqwa Prasetyaningrum

a)Universitas Sumatera Utara (USU)
b)Darma Cendika Catholic University
c)Mercu Buana University Yogyakarta
*denny.sihombing[at]ukdc.ac.id


Abstract

In North Sumatra, there are many rice fields and rice milling factories (Rice Milling Units). Rice Milling Units (RMU) are still many who have not applied the prediction method for the sale of rice so that it can affect the availability of raw materials. The purpose of this research is forecasting product sales (rice) at RMU Sumber Tani Tebing Tinggi so that it can know the number of raw materials needed so that will avoid idle time. To obtain optimal forecasting, it will be compared 2 (two) forecasting method, Linear Regression and Artificial Neural network with Backpropagation algorithm. The results showed the value of MSE on linear regression method of 214, while at the time using Artificial Neural Network obtained MSE value of 0.00099713. Based on the value of the MSE, it is seen that the smallest MSE is forecasting by Artificial Neural Network method. The results of forecasting sales of rice starting from June 2017 to May 2018 (in tonnes) are 98, 82, 85, 85,123, 91, 95, 98, 84, 124, 101, 75

Keywords: Artificial Neural Network, Sales Forecasting, Backpropagation, Linear Regression

Topic: Computer Engineering

Link: https://ifory.id/abstract/6A3NzVrCpk8W

Conference: Broad Exposure on Science and Technology (BEST 2019)

Plain Format | Corresponding Author (Denny Jean Cross Sihombing)

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