Optimasi Model Himpunan Keanggotaan Fuzzy Menggunakan Algoritme Evolution Strategies (Pada Data Diagnosis Penyakit Sapi Potong) - Dyan Putri Mahardika; Wayan Firdaus Mahmudy; Agus Wahyu Widodo (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
The increasing number of daily consumption of beef, it is need to monitoring the availability of good-quality beef, which is free from germs. In this research will be created a system that can be solve the subjective, unclear and vague problem, that is cattle disease diagnose, the existence of subjective information about the symptoms of the disease, then the diagnosis can be done using a fuzzy inference system. To obtaining the high accuracy, the set of fuzzy membership needs to be determined precisely. To get the best solution or in this case is the set of fuzzy membership can be done using Evolution Strategies method. The accuracy of the system is obtained by matching the results of the diagnosis and the diagnosis expert system using 51 generates test data accuracy rate of 95.49% using fuzzy. After optimization of the fuzzy set membership using evolution strategies (ES) produces better accuracy rate is 97.45% with the best parameters obtained ES is the population size of 80, offspring size of 10Î¼, and the amount of generation by 50.