Optimasi Performa K-Means melalui Hybrid Feature Engineering RFM dan Behavioral Analytics untuk Segmentasi Pelanggan: Optimizing K-Means Performance with Hybrid RFM and Behavioral Analytics for Customer Segmentation

Ilham B; Rahmaddeni Rahmaddeni; Aldino Putra; Dimas Najario; M. Aggie Fakhrizal

doi:10.57152/malcom.v5i4.2227

Authors

Ilham B Universitas Sains dan Teknologi Indonesia
Rahmaddeni Rahmaddeni Universitas Sains dan Teknologi Indonesia
Aldino Putra Universitas Sains dan Teknologi Indonesia
Dimas Najario Universitas Sains dan Teknologi Indonesia
M. Aggie Fakhrizal Universitas Sains dan Teknologi Indonesia

DOI:

https://doi.org/10.57152/malcom.v5i4.2227

Keywords:

Behavioral Analytics, Clustering, K-Means Clustering, Rekayasa Fitur, RFM, Segmentasi Pelanggan

Abstract

Membagi pelanggan menjadi beberapa segmen itu krusial untuk kesuksesan strategi pemasaran. Dalam studi ini, kami mengusulkan metode kombinasi dengan memanfaatkan algoritma K-Means yang dipadukan dengan metrik Recency, Frequency, Monetary (RFM) serta wawasan dari analitik perilaku. Tujuan utama kami adalah untuk mengetahui seberapa besar pengaruh penambahan fitur RFM terhadap kualitas segmen yang dihasilkan. Untuk itu, pendekatan kami meliputi pembersihan data transaksi ritel, pembuatan fitur berbasis perilaku, dan penerapan dua metode klastering: K-Means standar dan versi yang ditingkatkan, yaitu RFM K-Means Aware++. Hasil klastering dievaluasi dengan visualisasi t-SNE, analisis distribusi klaster, dan pengukuran metrik validasi internal seperti Silhouette Score dan Davies-Bouldin Index. Temuan kami menunjukkan bahwa metode yang lebih baik dengan fitur RFM menghasilkan klaster yang lebih stabil, terpisah dengan baik, dan lebih akurat dalam mencerminkan perilaku pelanggan. Sebaliknya, model yang tidak menggunakan fitur RFM cenderung membentuk klaster yang tumpang tindih dan memberikan segmentasi yang kurang bermakna. Secara keseluruhan, studi ini menekankan bahwa rekayasa fitur yang tepat memiliki peran penting dalam meningkatkan performa algoritma klastering dan menawarkan segmentasi pelanggan yang lebih berharga

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