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IJSTR >> Volume 6 - Issue 10, October 2017 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Ann Back Propagation For Forecasting And Simulation Hydroclimatology Data

[Full Text]

 

AUTHOR(S)

Syaefudin Suhaedi, Evi Febriana, Syaharuddin, HRP Negara

 

KEYWORDS

GUI, Matlab, ANN, Back Propagation, Hydroclimatology, Data

 

ABSTRACT

Government policies in distributing fertilizers and seeds of food crops such as rice and crops depend on the growing season of the farmers. Therefore, before conducting the distribution, it is necessary to spread early planting season in each region farmers so that the result of distribution is optimal. One of the alternatives that must be done first is to predict the pattern of hydroclimatological data cycle of the coming year to see the pattern of data of previous years. In this case required a method that can be used to predict the hydroclimatological data. The exact method used to make predictions is Artificial Neural Network (ANN) Back Propagation. As a follow-up step will be predicted by this ANN will be used to build system planning optimal cropping pattern for agricultural crops to avoid harvest failure (puso) in order to obtain maximum production results so as to support national food security. Based on the results of the simulation is known that ANN Back Propagation with two hidden layer are able to predict hydroclimatological data with an average accuracy of 95.72% - 96.61%. While the prediction validation obtained an average percentage error of 1.12% with the accuracy of 99.76%. The data used for training, testing, validation, and prediction are data in Central Lombok, NTB, Indonesia.

 

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