Open AccessOptimization of Fuzzy Tsukamoto Membership Function using Genetic Algorithm to Determine the River Water
Author(s) -
Qoirul Kotimah,
Wayan Firdaus Mahmudy,
Vivi Nur Wijayaningrum
Publication year - 2017
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v7i5.pp2838-2846
Subject(s) - crossover , genetic algorithm , fuzzy logic , value (mathematics) , membership function , function (biology) , computer science , mutation , population , algorithm , mathematical optimization , statistics , mathematics , fuzzy set , artificial intelligence , machine learning , biology , biochemistry , demography , evolutionary biology , sociology , gene
Some aquatic ecosystems in rivers depend on the river water, so it needs to be maintained by measuring and analyzing the river water quality. STORET is one of the methods used to measure the river water quality, but it takes a quite high of time and costs. Fuzzy Tsukamoto is an alternative method that works by grouping the river water data, but it is difficult to determine the membership function value. The solution offered in this study is the use of genetic algorithm to determine the membership function value of each criterion. Based on the test results, the optimization of fuzzy membership function using genetic algorithm provides higher accuracy value that is 95%, while the accuracy value without optimization process is 90%. The parameters used in genetic algorithm are as follows: population size is 80, generation number is 175, crossover rate ( cr ) is 0.6, and mutation rate ( mr ) is 0.4.