BACKPROPAGATION NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI HARGA KARET SPESIFIK TEKNIS - Puspa, Misrawati Aprilyana (Teknosains: Media Informasi Sains dan Teknologi, 2016)
Rubber is the commodity of the results of demand levels and agricultural 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.