Classification of prospective borrowing customers to reduce the risk of bad deposits in sharia cooperatives using the FK-NNC method

Abstract

Objective: Assisting cooperatives in determining the classification of prospective financing members to reduce non-performing deposits in sharia cooperatives
Design/method/approach: The Fuzzy K-Nearest Neighbor in Every Class method is used to classify prospective financing members. System development using the waterfall method.
Results: Based on the implementation and the results of tests carried out using the confusion matrix, the results show that using the Fuzzy K-Nearest Neighbor in Every Class method can classify prospective financing members with an average accuracy rate of 80% with a value of k=1 to k=10. Stable accuracy results of 80%. It shows that adding k theory to the Fuzzy K-Nearest Neighbor in Every Class method can improve the theory of assigning k values to the previous method, namely K-Nearest Neighbor and Fuzzy K-Nearest Neighbor.
Authenticity/state of the art: Based on previous research carried out, the research themes and characteristics are relatively the same, but in the research conducted, there are differences in terms of the methods used, case study data, preprocessing data, and research outputs. Previous research with the same object, namely the classification of cooperative customers by applying the K-Nearest Neighbor method by determining two classes of classification results, namely traffic jams and smooth, while this study will apply the development of the K-Nearest Neighbor method using the Fuzzy K-Nearest Neighbor in Every method. The class with the output specifies three outcomes: crash, sometimes crash, and smooth. This study uses the data preprocessing stage with fuzzification and data transformation techniques using the min-max normalization method. In contrast, the previous research used the z score normalization method.