Authors : Sugiono , Dewi Hardiningtyas and Lely Riawati
Abstract: The poor quality of management some farms today has been partly traced to inadequacies of risk analysis which engaging a lot of factors. By these reason, the purpose of the study is to deliver an intelligent tool that estimate directly the income of chicken meat and chicken egg farmer in any different situations. The research is started with study literature and early survey to identify various factors that may likely influence the benefit of chicken farmers. Such, factors as field areas, number of employers, time to harvesting, early modal, etc., were then used as input variable for system and factor of benefit was to be an output of the system. All the input and output information is used as database and Back Propagation Neural Network (BPNN) is trained to configure the complex correlation between input and output database. Finally, the software delivered an intelligent tool which provided the user to find quickly the prediction of the income in unique input without any investigations. Test data evaluation shows that BPNN model is able to correctly predict the benefit of chicken famers >90% of prospective farmers.
Sugiono , Dewi Hardiningtyas and Lely Riawati, 2014. Predicting the Income of Chicken Husbandry Using Artificial Neural Network: A Case Study of Chicken Farms in Blitar. International Business Management, 8: 190-195.