PENGARUH PARTICLE SWARM OPTIMIZATION PADA PELATIHAN BOBOT JARINGAN SYARAF TIRUAN UNTUK PERAMALAN TINGKAT INFLASI - , Harry Ganda Nugraha ([Yogyakarta] : Universitas Gadjah Mada, 2013)
Forecasting problem is a problem that often found in decision making process. The popular tool to handle that problem is artificial neural network. Artificial neural network are widely used because its ability to forecast nonlinear time series data. The learning algorithms that widely used to train artificial neural network weight is Backpropagation. But Backpropagation learning process sometimes encounter problem such as over fiting so it can not generalized the problem. Particle Swarm Optimization method is proposed to train artificial neural network weigth. The performance of each model will be measured by the Mean Square Error, Mean Absolute Percentage Error, Normalized Mean Square Error, Prediction Of Change In Direction, Average Relative Variance. Indonesia inflation time series data are used for analysis of model. The proposed method show that hybrid system could handle the time series forecasting problem as good as Backpropagation artificial neural network.