DISAIN JARINGAN SYARAF TIRUAN UNTUK PREDIKSI KUALITAS GULA KRISTAL PUTIH

Silvia, Evanila and Marimin, Marimin and Mahfud, Mahfud and Muhammad, Zein Nasution (2012) DISAIN JARINGAN SYARAF TIRUAN UNTUK PREDIKSI KUALITAS GULA KRISTAL PUTIH. In: Prosiding Seminar Nasional dan Rapat Tahunan Bidang Ilmu ilmu Pertanian BKS-PTN Wilayah Barat Tahun 2012. Fakultas Pertanian USU Medan, Medan, pp. 624-632. ISBN 9794586013

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Abstract

In this research, an Artificial Neural Network (ANN) based expert system for sugar’s quality prediction was developed by a learning’s method, backpropagation (BP). This system designed and developed using the software of Matlab 7.0.1 in a menu of simple interface called “SQP”. The constructing of the data’s input for ANN based on the fundamental parameters of sugar’s processing by using some expert’s advices and QFD’s method, consisting of the product’s attribute quality and the relevant process characteristics so this system be able to assess of sugar’s quality with more effective and efficient. Based on the test of “trial and error” of ANN’s training process, the best network performance for BP learning’s method obtained. The best network performance for BP was showed by the MSE score was 0.0098684 at the second epoh and the regretion’s coefficient was 1.0, this system used linear’s activation function (purelin), Levenberg-Marquadt’s algorithm training (trainlm), the momentum score was 0.05 and the minimum error was 0.01 with the network architecture or [35 20 1], that is, 35 neurons in an input layer, 20 neurons in a hidden layer and 1 neuro in an output layer. The implementation of this system was carried out using actual data obtained from PT. PG. Subang in a number of production periods in 2005. The result of SQP assessment showed that most of the sugar in the PT. PG. Subang production are in the first quality although in some observation’s period there are some sugar in the second quality

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