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MODEL JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI PERBANDINGAN TINGKAT PERTUMBUHAN KARET (Havea brasiliensis Muell Arg) KLON BPM 1, BPM 24 DAN PB 260 PADA FASE KEDUA

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Penilaian

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This study aims to test the artificial neural network model built to predict the growth of rubber plant clones BPM 1, BPM 24, and PB 260 in the second phase. The artificial neural network used in this study uses the backpropagation algorithm using Matlab 2018a. The artificial neural network architecture in this study uses 5 input layers, 1 hidden layer, 1 output layer, activation function used in this study is logsig as a hidden layer and purline function for the output layer. The rubber plants growth forecasting developed using an artificial neural network has a neuron structure of 16, 1 hidden layer, and the learning rate used is 0.1. training of artificial neural networks in forecasting the growth of rubber plants, the sample data used is 8 data from 12 data, and for network testing using 6 data samples from 12 data. After testing the network of each rubber clone, the BPM 24 regression value was R = 0.98889, PB 260 was R = 0.98117 and BPM 1 R = 0.98889. The Mean Square Error (MSE) value obtained in the BPM 24 clone was 0.00099992 on epoch 1296, PB 260 clone was 0.00099995 on epoch 2466, and the BPM 1 clone was 0.00099979 on epoch 1122. Evaluation of the network model using the MAPE equation on the BPM 1 is very good, with the MAPE value of shoot diameter, stem diameter, stem height, number of leaves, and tree height below less than 4%, but for MAPE clone PB 260 it was quite good with values below 13%, and for BPM 24 the stem height was still not good with a value of MAPE above 20%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207000638	T66609	T666092022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T666092022

Publisher

Inderalaya : Prodi Teknik Pertanian, Jurusan Teknologi Pertanian, Fakultas Pertanian Universitas Sriwijaya., 2022

Collation

xvi, 37 hlm.: ilus.: 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

678.207

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Teknik Pertanian
Teknologi Karet

Specific Detail Info

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Statement of Responsibility

SEPTA

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