Model Artificial Neural Network based on Particle Swarm Optimization Model For Inflation Rate Forecast - Dwi Raharjo, Joko S (Jurnal Sistem Komputer, 2013)
Prediction of inflation is needed by policy makers, investors and companies to plan economic strategies in anticipation of the inflation rate and financial planning in the future. Accurate prediction of the inflation rate will contribute to making the right decision. Many modeling used by researchers to obtain the best prediction accuracy is the most common and econometric models (eg AR, MA, ARIMA, etc.) but in the development, models of artificial neural network (ANN) from widely used because it proved that ANN models have better accuracy econometric models, especially compared to the predictions of inflation. ANN reliability further developed by several researchers through integration with other models, one of which integration between the ANN is optimized by particle swarm optimization (PSO). This integration is used to overcome weaknesses and improve the mutual advantages to each model in order to obtain a better measurement results. Testing capabilities in research, testing the ability of artificial neural network models (ANN), integrated with particle swarm optimization (PSO) based on the weight attribute or attribute weighting, hereinafter referred to awPSO-ANN. Test results show that the prediction rate of general inflation awPSO-ANN gives better RMSE value (0.157) compared ANN before the optimization (0.181)