Optimasi Batasan Fungsi Keanggotaan Fuzzy Tsukamoto Menggunakan Algoritme Genetika Untuk Kelayakan Pemberian Kredit (Studi Kasus: PD. BPR. Bank Daerah Lamongan) - Naily Zakiyatil Ilahiyah; Dian Eka Ratnawati; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Before the credit is given to the prospective debitor, the lender needs to select the prospective debitor's data first by considering several criteria. This is because the creditors get some problems that often occur when giving credit worthiness such as inconsistency to credit analysis that can change and the length of time required to select the data of prospective debitor due to the data that many and varied. These problems can be solved by building a classification system using the fuzzy tsukamoto method to classify the data and determine the creditworthiness of the debitor. However the use of the fuzzy tsukamoto method can not provide optimal results. It is shown with the accuracy value obtained is 90.476% from the test using 63 sample data. To obtain a more optimal accuracy, the workable solution is to optimize the fuzzy membership function constraint using genetic algorithm. Based on the results of testing system that has been optimized, the system obtained accuracy value of 93.651% with parameter popsize 220, Cr 0.7, Mr. 0.3 and generation number 220.