Sistem Pendukung Keputusan Pemberian Kredit berdasarkan Klasifikasi Kelancaran Pembayaran Kredit Menggunakan Metode VIKOR pada Bank XYZ - Daniel Hartono; Leo Willyanto Santoso; Silvia Rostianingsih (Universitas Kristen Petra, 2022)
Banks must carry out complex assessments before being able to determine who is the most eligible prospective debtor who can be given a loan. This is due to limited funds and the risk of bad credit cases. The limited manpower and manual processes cause the whole process of lending at XYZ Bank to be prone to human error and become inefficient. As a solution for XYZ Bank to overcome existing problems, a credit decision support system is needed that can assist XYZ Bank in selecting and determining prospective debtors who can be given loans. Therefore, in this study, the KNearest Neighbor method was used to assist XYZ Bank in predicting the smoothness of credit payments of a prospective debtor. Then, this research continues with ranking using the VIKOR method to determine who is the most ideal debtor candidate to be given a loan. Based on the results of the classification test using both training data and new data, the highest accuracy is obtained at 100% for each type of loan. Based on the results of the ranking test, the accuracy of the business loans ranking is 83.33%, the accuracy of the consumer loans ranking is 80.33%, and the accuracy of the various-purpose loans ranking is 70%. The results of the questionnaire evaluation in system testing conducted by 6 respondents assessed that the application design was 76.67% good, the application functionality was 86.67% good, the ease of use of the application was 83.33% good, the application answered the needs was 86.67% good, and the overall application was 90% good.