Penerapan Algoritme Genetika pada Optimasi Fungsi Keanggotaan Sistem Inferensi Fuzzy Tsukamoto untuk Diagnosis Penyakit HIV

Yobel Leonardo Tampubolon (Fakultas Ilmu Komputer, Universitas Brawijaya)
Lailil Muflikhah (Fakultas Ilmu Komputer, Universitas Brawijaya)
Indriati Indriati (Fakultas Ilmu Komputer, Universitas Brawijaya)

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 2 No 9 (2018): September 2018

One utilization of the fuzzy inference system is to diagnose HIV disease. In fuzzy inference system there is a membership function that plays an important role in solving the problem so that the function must be determined correctly and appropriately. Based on the rules and limitations of symptoms obtained from the expert used to establish the rules required in fuzzy logic to obtain accurate diagnosis of HIV disease. To obtain the right membership function can be done restrictions using Genetic Algorithm that can provide better accuracy results than the previous limitation. Genetic algorithm used can give accuracy about 45% for 24 data tested. Tests conducted using some of the best parameter values ​​there are, population value is 60, the generation is 40, crossover rate is 0.70 and mutation rate is 0.40. The optimization performed on fuzzy logic method using Genetic Algorithm has increased the accuracy about 20%.