Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/3247

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DC Field	Value	Language
dc.contributor.author	Hijratul, Aini
dc.contributor.author	Haviluddin, Haviluddin
dc.date.accessioned	2019-12-12T14:35:16Z
dc.date.available	2019-12-12T14:35:16Z
dc.date.issued	2019-06-02
dc.identifier.issn	2597-4602
dc.identifier.uri	http://repository.unmul.ac.id/handle/123456789/3247
dc.description.abstract	Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.	en_US
dc.language.iso	en	en_US
dc.publisher	Universitas Negeri Malang	en_US
dc.title	Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach	en_US
dc.type	Article	en_US
Appears in Collections:	J - Computer Sciences and Information Technology

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