PERAMALAN JUMLAH PERMINTAAN UDANG BEKU PND MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN (JST) BACKPROPAGATION
ABSTRACT: Forecasting is the
art or science to estimate how many needs will come in order to meet the demand
for goods or services, often based on historical time series data. The growing
number of emerging companies in Indonesia today has created a very tight
business competition in both servicesand products. Consumers choose the best
service and high quality and low price. Consumer demandis always uncertain or
varied in each subsequent period. The aim of this research was to determindthe
best backpropagation neural network architecture design and to predict the
demand of frozenproduct of PND 26/30. This research used the method of Neural
Network (ANN) and Processing ANN using MATLAB software. Implementation of ANN
method in PT.XYZ using Backpropagationalgorithm. Artificial neural network
architecture used was 12 input layer, 1 output layer, and 12 hidden layer and
activation function used tansig and purelin. Tansig for hidden layer and
purelin for output layer. The best artificial neural network architecture
design for product demand for PND 31/40 was a multi layer feedforward value of
Mean Square Error (MSE) network training value of 0.01 with MAPE 3.35. The
result of JST forecasting period 2017 were 960 MC, 637 MC, 572 MC, 993 MC, 1386
MC, 480 MC, 135 MC, 1209 MC, 1476 MC, 1029 MC, 290 MC, and 952 MC.
Penulis: Iid Mufaidah
Kode Jurnal: jppertaniandd170530
