Optimalisasi Layanan Keuangan Melalui Clustering Transaksi Bank dengan Metode K-Means

Abstract

This study uses the customer segmentation method using the K-means clustering technique and the Elbow Method to analyze bank customer transaction data. The goal is to understand the characteristics and behavior of customers or clients so that banks can provide services that are more in accordance with customer needs. This study uses a large number of customer data totaling 1,048,567 transactions. The selection of the optimal number of clusters is determined by the Elbow Method. The results of this study indicate that the K-Means Clustering method is indeed very effective for analyzing customer data segmentation. Of course it also allows banks to understand customers better. And also provide better service