The existence of banking institutions is very important for people's lives, especially used for raise funds in the form of deposits, demand deposits, savings and others. Besides that, it is an institution banks can also play a role as channeling funds in the form of credit to the public and business entities that need it. Problems in credit disbursement cause bad loans from customers, causing losses to the bank. Credit assessment is one of the important stages that must be carried out by the bank before credit is given to the credit applicant. The credit appraisal process belongs to a semi-structured problem that is quite complex, therefore it is necessary to develop a system to predict the feasibility of applying for credit. The system built in this study uses the K-Nearest Neighbor Classification algorithm by assessing prospective borrowers from the training data used. Based on the research that has been done, the K-Nearest Neighbor Classification algorithm can be modeled in a bank credit assessment. System testing results show accuracy of calculation accuracy of 81,82%.
Quantum K-nearest neighbor classification algorithm based on Hamming distance
