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Expert System Modeling for Land Suitability based on Fuzzy Genetic for Cereal Commodities: Case Study Wetland Paddy and Corn

Journal from gdlhub / 2016-11-17 04:39:21
By : Fitri Insani, Imas S Sitanggang, Marimin Marimin, Telkomnika
Created : 2015-09-01, with 1 files

Keyword : cereal; expert system; fuzzy; genetic algorithm; land suitability evaluation
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1735

Nowadays, threats of food shortages are happen in Indonesia. Most of crops that are consumed as main food are cereals commodities. Cereals cultivation often experience some problems in determining whether land is suitable or not for the crops. Expert system can help researcher and practitioners to identify land suitability for cereal crops. In this research, an expert system model of land suitability for cereals crop was built. The model implemented soft computing methods to develop inference engine which combines fuzzy system and genetic algorithm. There are 16 parameters to define land suitability which consists of 12 numeric parameters and 4 categorical parameters. Two types of cereal crops that were used in this study namely wetland paddy and corn. Trapezoid membership function was used to represent fuzzy sets for numerical parameters. Genetic algorithm was used for tuning the membership function of fuzzy set for land suitability which consists of very suitable (S1), quite suitable (S2), marginal suitable (S3) and not suitable (N). This expert system is able to choose land suitability classes for cereals using the fuzzy genetic model with accuracy of 90% and 85% for corn and wetland paddy respectively.

Description Alternative :

Nowadays, threats of food shortages are happen in Indonesia. Most of crops that are consumed as main food are cereals commodities. Cereals cultivation often experience some problems in determining whether land is suitable or not for the crops. Expert system can help researcher and practitioners to identify land suitability for cereal crops. In this research, an expert system model of land suitability for cereals crop was built. The model implemented soft computing methods to develop inference engine which combines fuzzy system and genetic algorithm. There are 16 parameters to define land suitability which consists of 12 numeric parameters and 4 categorical parameters. Two types of cereal crops that were used in this study namely wetland paddy and corn. Trapezoid membership function was used to represent fuzzy sets for numerical parameters. Genetic algorithm was used for tuning the membership function of fuzzy set for land suitability which consists of very suitable (S1), quite suitable (S2), marginal suitable (S3) and not suitable (N). This expert system is able to choose land suitability classes for cereals using the fuzzy genetic model with accuracy of 90% and 85% for corn and wetland paddy respectively.

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