Identifying the Quality System of Cocoa Beans to Increase Productivity Using Backpropagation Neural Network Algorithm: A Case Study at Sumber Rejeki Farmers Group, Patuk Gunung Kidul

An image is a two-dimensional function (x, y) consisting of a number of finite elements. Image processing uses Backpropagation and Principal Component Analysis (PCA) Artificial Neural Networks. The processed data is the image data of 256 x 256 pixel cocoa beans taken from the Sumber Rejeki Farmer Gr...

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Published inJournal of physics. Conference series Vol. 1413; no. 1; pp. 12033 - 12042
Main Authors Lestari, U, Kumalasanti, R A, Wulandari, E S
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.11.2019
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ISSN1742-6588
1742-6596
1742-6596
DOI10.1088/1742-6596/1413/1/012033

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Abstract An image is a two-dimensional function (x, y) consisting of a number of finite elements. Image processing uses Backpropagation and Principal Component Analysis (PCA) Artificial Neural Networks. The processed data is the image data of 256 x 256 pixel cocoa beans taken from the Sumber Rejeki Farmer Group (KTSR) with the results of two types of identification output, namely quality cocoa beans and non-quality cocoa beans. The identification process in the system through two stages of the process, namely the training process where the image data of cocoa beans through the training process with input parameters ANN. Image data that has been completed through the training process is used for the testing process. Based on the results of the study using the Backpropagation method with parameters alpha value = 0.35, tolerance value error = 0.05, iteration value = 1000, input neuron value = 255, hidden neuron value = 6, output neuron value = 3 produce quality image data 14 and 6 quality images that are not qualified from the test image data are 20 images of cocoa beans with an accuracy rate of 70% and an error rate of 30%. Based on the tests that have been conducted, it can be concluded that the ANN Backpropagation algorithm can produce a classification of cacao seed quality with a high degree of accuracy
AbstractList An image is a two-dimensional function (x, y) consisting of a number of finite elements. Image processing uses Backpropagation and Principal Component Analysis (PCA) Artificial Neural Networks. The processed data is the image data of 256 x 256 pixel cocoa beans taken from the Sumber Rejeki Farmer Group (KTSR) with the results of two types of identification output, namely quality cocoa beans and non-quality cocoa beans. The identification process in the system through two stages of the process, namely the training process where the image data of cocoa beans through the training process with input parameters ANN. Image data that has been completed through the training process is used for the testing process. Based on the results of the study using the Backpropagation method with parameters alpha value = 0.35, tolerance value error = 0.05, iteration value = 1000, input neuron value = 255, hidden neuron value = 6, output neuron value = 3 produce quality image data 14 and 6 quality images that are not qualified from the test image data are 20 images of cocoa beans with an accuracy rate of 70% and an error rate of 30%. Based on the tests that have been conducted, it can be concluded that the ANN Backpropagation algorithm can produce a classification of cacao seed quality with a high degree of accuracy
Author Wulandari, E S
Lestari, U
Kumalasanti, R A
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StartPage 12033
SubjectTerms Algorithms
Artificial neural networks
Back propagation
Back propagation networks
Cocoa
Cocoa beans
Image processing
Image quality
Neural networks
Physics
Principal components analysis
Process parameters
Training
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Title Identifying the Quality System of Cocoa Beans to Increase Productivity Using Backpropagation Neural Network Algorithm: A Case Study at Sumber Rejeki Farmers Group, Patuk Gunung Kidul
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