Klasifikasi Tingkat Risiko Penyakit Stroke Menggunakan Metode GA-Fuzzy Tsukamoto - Vina Adelina; Dian Eka Ratnawati; Mochammad Ali Fauzi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Stroke is clinical syndrome which usually comes sudden, quick, in a form of focal or global neurological deficits that happen within 24 hours or sometimes can cause a death. Stroke problems in Indonesia need a serious attention because of the number of death is high and always inCreasing. On of the necessary handling is detecting the symptoms of stroke in a form of SKD (Sistem Kewaspadaan Dini). Research found that to estimate the risk of stroke, it can use Fuzzy logic inference. From the 15 data test that has been done, the result gets 60% accuration. To optimize the result of membership degree function, it uses genetics algorithm in Fuzzy tsukamoto inference. Representation of chromosomes used is real code which every chromosome initialize the limitations in all Fuzzy variables. Crossover method using one cut point, random mutation used for mutation method and elitism selection used for election method. It is known that the result from optimization from the system accuration using Fuzzy tsukamoto-GA is 86.66% and the number of popsize which from the best parameter of the optimum result is 500, and the number of generations is 1000 as well as the combination Cr = 0,5 and Mr= 0,6. Keywords: stroke, genetics algorithm, Fuzzy tsukamoto, classification