Analisis Risiko Kredit Perbankan Menggunakan Algoritma K-Nearest Neighbor dan Nearest Weighted K-Nearest Neighbor

Dian Tri Wilujeng (Jember University)
Mohamat Fatekurohman (Jember University)
I Made Tirta (Jember University)

Indonesian Journal of Applied Statistics
Vol 5, No 2 (2022)

Bank is a business entity that collects public funds in the form of savings and also distributes them to the public in the form of credit or other forms.  Credit risk analysis can be done in various ways such as marketing analysis and big data using machine learning.  One example of a machine learning algorithm is K-Nearest Neighbor (KNN) and the development of the K-Nearest Neighbor algorithm is Neighbor Weighted KNearest Neighbor (NWKNN).  The K-Nearest Neighbor (KNN) algorithm is one of the machine learning methods that can be used to facilitate the classification of complex data.  The purpose of this study is to determine the results of the application of the algorithm and the comparison of the use of the KNN and NWKNN algorithms in banking credit.  The results obtained are that NWKNN is able to predict credit risk better, especially in classifying potential customers with potential losses compared to KNN. Keywords: Machine learning, KNN, NWKNN