Optimasi Jumlah Pinjaman Koperasi Menggunakan Fuzzy Tsukamoto Dengan Algoritme Genetika - Shelly Puspa Ardina; Imam Cholissodin; Budi Darma Setiawan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Nowadays, almost the majority of cooperatives are still performing calculations lending manually and very rarely utilize the use of computer technology so that in decision making is done less efficient. From the problems encountered, it takes a system that has a relationship with the computer so that it can accelerate and assist the process of making the decision of lending efficiently. The required system is an artificial intelligence system that helps to get the most precisely seen value of the greatest fitness as each of its calculations. The criteria that become the basis for determining the loan amount to the members using the optimized Tsukamoto Fuzzy method using Genetic Algorithm are job status, age, salary and loan duration. The results will be able to show the fitness in each of the calculations that have been optimized by using Genetic Algorithm, so it will get the most appropriate value. The result of system evaluation using Mean Absolute Percentage Error calculation with the example of a case has an error value of 0.661468035 or 1.65% with the resulting fitness value of 0.601877363.