Segmentasi Pelanggan Majalah pada Situs Web E-Commerce dengan K-Means++ dan Metode RFM
DOI:
https://doi.org/10.25126/jtiik.1168208Kata Kunci:
Segmentasi Pelanggan, E-Commerce, K-Means++, RFM, CRISP-DMAbstrak
Segmentasi pelanggan merupakan salah satu metode yang dapat diterapkan untuk memaksimalkan peluang bisnis. Hal tersebut dapat membantu bisnis agar tetap kompetitif dalam persaingan pasar. Penerapan Artificial Intelligence (AI) dapat membantu dalam memberikan pemahaman kepada pelaku bisnis tentang segmentasi pelanggan berdasarkan riwayat transaksi. Penelitian ini menerapkan metode Recency, Frequency, and Monetary (RFM) yang dipadukan dengan algoritma clustering K-Means++ untuk melakukan segmentasi pelanggan. Silhouette score menjadi indikator pemilihan nilai k yang paling optimal dalam menentukan jumlah cluster. Kerangka kerja CRISP-DM yang digunakan dalam makalah ini juga membantu mempertahankan proses analisis yang konsisten. Pendekatan statistik sederhana ddigunakan untuk mengklasifikasikan setiap fitur dalam RFM menjadi label low, medium, dan high dalam hal menangkap pola segmentasi pelanggan. Hasil eksperimen menunjukkan nilai k = 3 sebagai yang paling optimal berdasarkan nilai WSS sebesar 843,214747 dan silhouette score sebesar 0,638181. Eksperimen juga menunjukkan bahwa cluster 0 memiliki nilai RFM rata-rata sebesar 1,14 (low), 1,20 (low), dan 301.640 (low). Cluster 1 memiliki nilai RFM rata-rata sebesar 249,61 (high), 2,62 (medium), dan 799,934 (medium). Cluster 2 memiliki nilai RFM rata-rata sebesar 233,01 (medium), 6,41 (high), dan 2018,088 (high).
Abstract
Customer segmentation is one method that can be applied to maximize business opportunities. It can help businesses remain competitive in the market competition. The application of Artificial Intelligence (AI) can assist in providing business stakeholders with an understanding of customer segmentation based on transaction history. This study applies the Recency, Frequency, and Monetary (RFM) method combined with the K-Means++ clustering algorithm for customer segmentation. The Silhouette score serves as an indicator for selecting the most optimal value of k to determine the number of clusters. The CRISP-DM framework used in this paper also helps maintain a consistent analysis process. A simple statistical approach is used to classify each RFM feature into low, medium, and high labels to capture customer segmentation patterns. Experimental results show that k = 3 is the most optimal value based on a WSS value of 843.214747 and a silhouette score of 0.638181. The experiments also indicate that Cluster 0 has average RFM values of 1.14 (low), 1.20 (low), and 301,640 (low). Cluster 1 has average RFM values of 249.61 (high), 2.62 (medium), and 799,934 (medium). Cluster 2 has average RFM values of 233.01 (medium), 6.41 (high), and 2018.088 (high).
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