Abstract

In the last five years (2013-2017) Indonesia’s fertilizer production experienced volatile growth, but overall tended to increase at a rate of 1.7% per year. The research aims to optimize artificial neural networks with a resilient backpropagation algorithm (Rprop), artificial neural networks are one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Sample data used for optimization is fertilizer import data according to the main country of origin and uses 4 architectures, the best results are obtained between architectures 6-8-1, 6-12-1, 6-16-1, and 6-32-1 is architecture 6-32-1 with 100% accuracy.

Details

Title
Resilient Algorithm In Predicting Fertilizer Imports by Major Countries
Author
Solikhun 1 ; Wahyudi, Mochamad 1 ; Safii, M 1 ; Zarlis, Muhammad 2 

 Doctoral Program, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Sumatera Utara, Indonesia. 
 Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia 
Publication year
2020
Publication date
Feb 2020
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2562154369
Copyright
© 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.