Segmentasi Pelanggan E-Commerce Berbasis Perilaku Belanja Menggunakan Algoritma K-Means Clustering dan Evaluasi Silhouette Score
- Muhammad Yusran Universitas Budi Darma, Medan, Indonesia
-
Rizkah Fadillah
*
Politeknik Cendana, Medan,
Indonesia
- Mesran Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Indonesia
- Raymond Shawn Politeknik Cendana, Medan, Indonesia
Abstract
Dalam era persaingan E-Commerce yang ketat, pemahaman mendalam mengenai perilaku konsumen menjadi kunci utama keberhasilan strategi pemasaran. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan toko online menggunakan algoritma K-Means Clustering berbasis perilaku belanja. Menggunakan dataset Mall Customer Segmentation, penelitian ini memproses fitur utama berupa Pendapatan Tahunan dan Skor Pengeluaran yang dinormalisasi. Evaluasi model dilakukan menggunakan metode Elbow dan Silhouette Score untuk menentukan jumlah kelompok optimal. Hasil penelitian menunjukkan bahwa pembagian pelanggan menjadi 5 cluster adalah yang paling optimal dengan nilai Silhouette Score sebesar 0.5547. Kelima segmen yang terbentuk meliputi Middle Class (40,5%), VIP/Big Spenders (19,5%), Hemat Mapan (17,5%), Ekonomis (11,5%), dan Impulsif (11%). Temuan ini memberikan wawasan strategis bagi pelaku bisnis untuk merancang kampanye pemasaran yang terpersonalisasi, seperti program loyalitas eksklusif untuk segmen VIP dan penawaran diskon intensif untuk segmen Ekonomis, guna meningkatkan retensi dan profitabilitas
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