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|dc.description.abstract||This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production. © 2006-2015 Asian Research Publishing Network (ARPN).||-|
|dc.title||Utilization of artificial immune system in prediction of paddy production||en_US|
|Appears in Collections:||IPRE Scholarly Publication|
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