Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

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 b...

Full description

Bibliographic Details
Main Authors: Hijratul Aini, Haviluddin Haviluddin
Format: Article
Language:English
Published: Universitas Negeri Malang 2019-06-01
Series:Knowledge Engineering and Data Science
Online Access:http://journal2.um.ac.id/index.php/keds/article/view/7335
id doaj-c7eb4b49ff1541f3ada1d3c7881f34d9
record_format Article
spelling doaj-c7eb4b49ff1541f3ada1d3c7881f34d92020-11-25T03:47:21ZengUniversitas Negeri MalangKnowledge Engineering and Data Science2597-46022597-46372019-06-01211910.17977/um018v2i12019p1-93718Crude Palm Oil Prediction Based on Backpropagation Neural Network ApproachHijratul AiniHaviluddin HaviluddinCrude 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.http://journal2.um.ac.id/index.php/keds/article/view/7335
collection DOAJ
language English
format Article
sources DOAJ
author Hijratul Aini
Haviluddin Haviluddin
spellingShingle Hijratul Aini
Haviluddin Haviluddin
Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach
Knowledge Engineering and Data Science
author_facet Hijratul Aini
Haviluddin Haviluddin
author_sort Hijratul Aini
title Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach
title_short Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach
title_full Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach
title_fullStr Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach
title_full_unstemmed Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach
title_sort crude palm oil prediction based on backpropagation neural network approach
publisher Universitas Negeri Malang
series Knowledge Engineering and Data Science
issn 2597-4602
2597-4637
publishDate 2019-06-01
description 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.
url http://journal2.um.ac.id/index.php/keds/article/view/7335
work_keys_str_mv AT hijratulaini crudepalmoilpredictionbasedonbackpropagationneuralnetworkapproach
AT haviluddinhaviluddin crudepalmoilpredictionbasedonbackpropagationneuralnetworkapproach
_version_ 1724502346318217216

Similar Items