Open AccessResilient Algorithm In Predicting Fertilizer Imports by Major Countries
Author(s) -
. Solikhun,
Mochamad Wahyudi,
M. Safii,
Muhammad Zarlis
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/769/1/012038
Subject(s) - rprop , artificial neural network , fertilizer , backpropagation , computer science , artificial intelligence , process (computing) , production (economics) , sample (material) , algorithm , economics , types of artificial neural networks , recurrent neural network , agronomy , biology , chemistry , macroeconomics , chromatography , operating system
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.