Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam

Abstract

Apart from that, there are efforts to provide for the needs of its members as well as financial assistance for education, health and there are also concessions needed by the members. By conducting this customer cluster, it will help the company determine its potential customers so that it can implement the right marketing strategy for each type of existing customer, and will certainly provide benefits for the company in increasing the quality and loyalty of customers towards the company. Data mining has functions, namely prediction, description, classification and clustering functions. Data mining also has many methods for its application, one of these methods is K-Means. The K-Means Clustering algorithm can be implemented in grouping potential customers, especially in savings and loan cooperatives. Based on the data sampling used, the data can be grouped into 2 (two) clusterings.