Artificial neural network based particle swarm optimization in predictions mortality rate of broiler chicken

Abstract

Success in broiler farms can be assessed from the mortality rate of chickens and also a solution to reduce the mortality rate of chickens. But in the process there are obstacles that is the death of hickens that tend to fluctuate so that of course resulted in financial losses for farmers. To prevent it required a method that can predict and control the mortality rate of broiler chickens. In this research, data mining method used is Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO), to produce accurate prediction with excellent iteration and also small error rate. The results of analysis in predicting mortality rate of broiler chickens by artificial neural network method in combination with Particle swarm optimization get better RMSE result (0.135) than Artificial Neural Network have not been optimized (0.381) with january mortality data and result of application quisioner made value 83,125 which can be categorized well and enough help the farmer in controlling prediction of chicken mortality rate broiler.