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dc.contributor.authorMUFAIDAH, Iid
dc.contributor.authorSUWASONO, Sony
dc.contributor.authorWIBOWO, Yuli
dc.contributor.authorSOEDIBYO, Deddy Wirawan
dc.date.accessioned2020-09-10T04:10:58Z
dc.date.available2020-09-10T04:10:58Z
dc.date.issued2017-06-01
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/100938
dc.description.abstractForecasting 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 services and products. Consumers choose the best service and high quality and low price. Consumer demand is always uncertain or varied in each subsequent period. The aim of this research was to determind the best backpropagation neural network architecture design and to predict the demand of frozen product 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 Backpropagation algorithm. 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.en_US
dc.language.isoInden_US
dc.publisherJurnal Agroteknologi Vol. 11 No. 01 (2017)en_US
dc.subjectartificial neural networken_US
dc.subjectPND 26/30en_US
dc.subjectbackpropagationen_US
dc.subjectMSEen_US
dc.subjectMAPEen_US
dc.titlePeramalan Jumlah Permintaan Udang Beku PND Menggunakan Metode Jaringan Syaraf Tiruan (JST) Backpropagation (Forecasting of PND Frozen Shrimp Demand Using Artificial Neural Network Method (ANN) Backpropagation)en_US
dc.typeArticleen_US
dc.identifier.kodeprodiKODEPRODI1710101#Teknologi Hasil Pertanian
dc.identifier.kodeprodiKODEPRODI1710102#Teknologi Industri Pertanian
dc.identifier.kodeprodiKODEPRODI1710201#Teknik Pertanian
dc.identifier.nidnNIDN0009116405
dc.identifier.nidnNIDN0030077202


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