Predicting Non-Performing Loan's Risk Level Using KMeans Clustering and K-Nearest Neighbors

Muhammad Mizan Siregar (Magister of Computer Science, Potensi Utama University)
Roslina Roslina (Departement of Computer and Informatics Technology, Politeknik Negeri Medan)
B. Herawan Hayadi (Magister of Computer Science, Potensi Utama University)

Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In

In data mining, clustering is an unsupervised learning technique often used to group data by similarity. Clustering, especially the K-means clustering algorithm, is a feasible tool for expanding a dataset label by increasing the cluster's number according to the label's categories. This research extends the credit loan label data set from two categories (non-performing and performing loans) to four risk levels (high risk, medium risk, low risk, and no risk). The combination of three K-nearest neighbor’s distance metrics, Euclidean, Manhattan, and Chebyshev distance, with four different K values (K = 3, K = 5, K = 7, and K = 9) produced the best model with accuracy, precision, and recall values of 90%, 90.53571%, and 90%, from the model using the Euclidean distance with K = 9