Penerapan Algoritma K-Means Clustering untuk Segmentasi Nasabah Bank
Abstract
Segmentasi nasabah merupakan strategi yang penting dalam industri perbankan untuk memahami karakteristik dan kebutuhan pelanggan secara lebih mendalam. Penelitian ini menerapkan algoritma K-Means clustering untuk mengelompokkan nasabah berdasarkan faktor demografi dan finansial, seperti usia, jenis kelamin, pendapatan, status pernikahan, dan kepemilikan aset perbankan. Data yang digunakan terdiri dari 600 record nasabah yang diambil dari sumber daring dan diproses menggunakan metode unsupervised learning dalam data mining. Proses clustering dilakukan dengan beberapa nilai k (jumlah cluster) untuk menentukan pengelompokan yang optimal. Validasi dilakukan menggunakan Within-Cluster Sum of Squares (WCSS) dan Davies-Bouldin Index guna mengukur kualitas hasil segmentasi. Hasil penelitian menunjukkan bahwa segmentasi dengan nilai k = 2 dan k = 4 memberikan pola yang lebih jelas dibandingkan k = 3, di mana setiap cluster memiliki karakteristik yang berbeda dalam hal profil risiko, preferensi produk, dan tingkat keterlibatan dengan layanan perbankan. Dengan pendekatan ini, bank dapat meningkatkan strategi pemasaran, manajemen risiko kredit, serta personalisasi layanan berdasarkan segmentasi yang dihasilkan. Selain itu, penelitian ini memberikan rekomendasi untuk pengembangan sistem berbasis data mining yang dapat membantu dalam pengambilan keputusan bisnis di sektor perbankan.
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