Pemanfaatan Data Mining untuk Segmentasi Nasabah Kartu Kredit Menggunakan Metode K-Means - Intan Pramesta Nurhayati; Helmayana; Adis Tiani; Kezia Maruenci; Yuriana Sari Harahap; Maulana Fansyuri (PT Jurnal Cendekia Indonesi, 2025)
This study aims to cluster credit card users based on demographic information and card usage behavior using K-Means clustering algorithms. The BankChurners.xlx dataset, which contains over 10,000 customer data, was analyzed using RapidMiner software. The analysis process includes data preprocessing steps, including normalization, attribute selection, and categorical data encoding. The K-Means algorithm is then used to group customers into two clusters. The results of this clustering show the existence of two main segments with different characteristics, where the majority of customers fall into one larger group. Cluster quality assessment using the Davies-Bouldin index shows satisfactory separation results. This result can serve as a basis for strategic decision-making, particularly in designing marketing plans and developing services that are more precise and suited to the characteristics of each customer segment.