Jaringan Syaraf Tiruan untuk Memprediksi Penjualan Kelapa Sawit Menggunakan Algoritma Backpropagation

Abstract

This study discusses the prediction of palm oil sales using artificial neural networks, which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses a backpropagation algorithm where the data entered is the number of sold. Then artificial neural networks are formed by determining the number of units per layer. After the networks is formed, training is carried out from the grouped data. Experiments are carried out with an architecture consisting of input units, hidden units, output units and architecture. Testing is done with matlab software. For now the competition for palm oil sales is getting tougher. Predictions with the best accuracy use the 12-2-1 architecture with an accuracy rate of 92% and the lowest level of accuracy using 12-6-1 architecture with an accuracy rate of 58%