BACKPROPAGATION NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI HARGA KARET SPESIFIK TEKNIS
Abstract
Rubber is the commodity of the results of demand levels andagricultural production always has increased significantly from time to time.
This is due to the high demand of the company's suppliers are the result of
processed rubber to meet the needs of production. However, due to the
influence of the conditions of the global economy so that it appears the
instability of prices. The data used in this research in the form of a
Univariate time series data is converted into the multivariate. The method
used is the method of Back propagation Neural Network (BPNN) is applied
to the data time series technical specific rubber commodity prices with the
help of weighted optimization Particle Swarm Optimization (PSO) which
hopefully may help to improve the performance of the prediction so that
results of the RMSE for the prediction of rubber prices gained can be more
accurate. Of research results obtained the best model on a back propagation
neural network with the parameters for the training cycle 600, the learning
rate and momentum 0.1 0.2, as well as neuron size 3 whereas in particle
swarm optimization value of population size 8, max value. of generation 100,
the value of inertia weight 0.3, the value of the local best weight 1.0 and
global best value weight of 1.0 produces a better RMSE value i.e. 0.040
compared to just using the BPNN alone i.e. 0043. This proves that the PSO
method able to give better results.
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