Visualisasi Segmentasi Pelanggan Berdasarkan Atribut RFM Menggunakan Algoritma K-Means Untuk Memahami Karakteristik Pelanggan pada Toko Retail Online
DOI:
https://doi.org/10.25126/jtiik.2025128619Kata Kunci:
looker studio, segmentasi pelanggan, K-Means, RFM, retail online, visualisasi dataAbstrak
Meningkatnya minat belanja pelanggan toko retail online menimbulkan persaingan ketat antar retailer. Agar tetap unggul dan kompetitif, retailer perlu memahami karakteristik pelanggannya. Penerapan segmentasi pelanggan memberikan kemudahan pada retailer untuk memahami karakteristik pelanggan berdasarkan penilaian pada atribut yang dihitung dari data riwayat transaksi pelanggan. Hasil segmentasi pelanggan yang divisualisasikan dapat meningkatkan pemahaman retailer dalam memahami data dan membantu dalam proses pengambilan keputusan. Oleh karena itu, penelitian ini mengusulkan Visualisasi Segmentasi Pelanggan menggunakan Algoritma K-means berdasarkan Atribut RFM (Recency, Frequency, Monetary). Hasil segmentasi dapat digunakan untuk Memahami Karakteristik Pelanggan pada Toko Retail Online. Penelitian ini menggunakan algoritma k-means untuk menjalankan clustering yang performanya akan dibandingkan dengan algoritma k-medoids mengacu pada nilai silhouette, Calinski-Harabasz Index, dan DaviesBouldin Index dalam melakukan segmentasi pelanggan berdasarkan atribut RFM. Berdasarkan metrik tersebut, didapatkan nilai algoritma k-means berturut-turut adalah 0,6558, 0,7219, dan 3578,9, sedangkan nilai algoritma k-medoids adalah 0,4677, 0,8298, dan 1236,9. Dengan demikian, hasilnya menunjukkan bahwa kinerja clustering menggunakan k-means lebih baik daripada menggunakan k-medoids. Pada dashboard Looker Studio ditampilkan visualisasi data hasil segmentasi tersebut, kemudian diuji fungsionalitasnya dengan metode Blackbox Testing dan berhasil menyelesaikan semua skenario pengujian, kemudian dilakukan pengujian dengan metode UAT (User Acceptance Testing) dan mendapatkan predikat sangat layak.
Abstract
The growing interest in online retail shopping among customers has resulted in intense competition among retailers. To sustain a competitiveness, retailers need to understand characteristics of their customer. Implementation of customer segmentation facilitates retailers in understanding customer characteristics through assessments based on attributes derived from customer transaction history data. Visualization of customer segmentation results can enhance the retailer's understanding of data and assist in the decision-making process. Therefore, this study proposes the Visualization of Customer Segmentation using the K-means Algorithm based on RFM Attributes (Recency, Frequency, Monetary). The segmentation results can be utilized to understand the characteristics of customers in an online retail store. This study explores the k-means algorithm to execute clustering, and its performance will be compared with the k-medoids algorithm, based of silhouette values, Calinski-Harabasz Index, and Davies Bouldin Index in customer segmentation based on RFM attributes. Based on given metrics, the consecutive performance values for k-means algorithm are 0.6558, 0.7219, and 3578.9, while k-medoids algorithm are 0.4677, 0.8298, and 1236.9. Thus, the results indicate that the clustering performance using k-means is better than using k-medoids. On the Looker Studio dashboard, the visualization of the segmentation data is displayed, and its functionality is tested using the Black Box Testing method, successfully completing all test scenarios. Subsequently, the system undergoes testing through the User Acceptance Testing (UAT) method and receives a highly satisfactory rating.
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