Implementasi Algoritma Genetika untuk Optimasi LVQ pada Penentuan Kelayakan Kredit (Studi Kasus: Bank X) - Aghata Agung Dwi Kusuma Wibowo; Candra Dewi; Indriati Indriati (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
In determining debtor credit, if there is an error in the debtor's analysis, it will cause problems such as bad credit in the future. So, it needs more accurate selection in the analysis of debtors who deserve credit. A more rigorous and consistent analysis takes longer due to the large amount of analytical data. To obtain a more accurate analysis and more efficient analysis time, it can be done by making a credit analysis system using the Learning Vector Quantization (LVQ) method to classify data and determine debits that are eligible for credit. To obtain accurate credit results, the use of the LVQ method depends on the weight. Analysis with LVQ method shows the accuracy value obtained is 79.37% by testing 63 test data. To obtain optimal accuracy values, the weights used in the LVQ method are optimized first with genetic algorithms. Optimal weight test results obtained a higher accuracy value of 93.65% for testing with popsize 20 parameters, Cr 0.9, Mr 0.1 and number of generation 10.