APLIKASI PREDIKSI KELAYAKAN CALON ANGGOTA KREDIT PADA KSPPS BMT ARTA JIWA MANDIRI WONOGIRI MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR

An economy that tends to be unstable causes many people to make loans at banks and cooperatives to meet their increasing daily needs. But there are some people who cannot return the loan in a timely manner. These problems can be created or developed by an application that is used to predict whether the people who apply for loans can return loans smoothly, smoothly and stall. Use of attributes such as gender, age, type of work, number of loans, term of return, collateral and income and use the K-Nearest Neighbor algorithm to make predictions. From the research results obtained in the form of accuracy value of 80%, recall of 91% and preciison of 85%. Thus this application can be used to help the pinjman savings cooperative in considering prospective savings and loan credit members who deserve a capital loan. Keywords: data mining, K Nearest Neighbor, cooperatives, savings and loans.