ANALISIS SEGMENTASI PELANGGAN BERBASIS MODEL RECENCY FREQUENCY DAN MONETARY (RFM) MENGGUNAKAN ALGORITMA K-MEANS
DOI:
https://doi.org/10.23960/jitet.v11i3s1.3396Abstract Views: 372 File Views: 357
Abstract
Business development is currently growing very rapidly, with the development of internet technology that can facilitate all business activities. Increasing business development has an impact on presenting new business competitors, so companies need strategies that are able to maintain customer quality. This study aims to segment customers from the company's sales transaction data, with a large number of transactions, technology is needed to group a data so that the method used in this study is a data mining method and uses the K-Means algorithm. With the K-Means Algorithm, it can help in grouping customers to make it easier for companies to strategize each customer group. This customer grouping uses an initial model of Recency, Frequency and Monetary (RFM) to help calculate customer groups. Data mining evaluation was carried out using Silhouette Coefficient with test results using Visual Studio Code software python programming language, The results of this study selected 3 clusters consisting of Low Loyalty totaling 137 customers, Medium Loyalty totaling 1636 customers and Highest Loyalty totaling 2395 customers.Downloads
References
M. Jordy, A. Triayudi, and I. D. Sholihati, “Analisis Segmentasi Recency dan Customer Value Pada AVANA Indonesia Dengan Algoritma K-Means dan Model RFM ( Recency , Frequency and Monetary ),” vol. 4, no. 2, pp. 579–589, 2023, doi: 10.47065/josh.v4i2.2950.
B. Rizki, N. G. Ginasta, M. A. Tamrin, and A. Rahman, “Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means,” J. Online Inform., vol. 5, no. 2, p. 130, 2020, doi: 10.15575/join.v5i2.511.
R. Siagian, P. Sirait, and A. Halim, “E-Commerce Customer Segmentation Using K-Means Algorithm And LRFM Model,” JITE (Journal Informatics Telecommun. Eng., vol. 5, no. 1, pp. 21–30, 2021, [Online]. Available: http://ojs.uma.ac.id/index.php/jite
B. Basri, W. Gata, and R. Risnandar, “Analisis Loyalitas Pelanggan Berbasis Model Recency, Frequency, dan Monetary (RFM) dan Decision Tree pada PT. Solo,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 5, p. 943, 2020, doi: 10.25126/jtiik.2020752284.
A. Febriani and S. A. Putri, “Segmentasi Konsumen Berdasarkan Model Recency, Frequency, Monetary dengan Metode K-Means,” JIEMS (Journal Ind. Eng. Manag. Syst., vol. 13, no. 2, pp. 52–57, 2020, doi: 10.30813/jiems.v13i2.2274.
M. Fithriyah, M. A. Yaqin, and S. Zaman, “K-Means Clustering Untuk Segmentasi Produk Berdasarkan Analisis Recency, Frequency, Monetary (RFM) Pada Data Transaksi Penjualan,” Ilk. J. Comput. Sci. Appl. Informatics, vol. 3, no. 2, pp. 151–164, 2021, doi: 10.28926/ilkomnika.v3i2.284.
Nofyat, A. Ibrahim, and A. Ambarita, “Sistem Informasi Pengaduan Pelanggan Air Berbasis Website Pada Pdam Kota Ternate,” IJIS - Indones. J. Inf. Syst., vol. 3, no. 1, p. 10, 2018, doi: 10.36549/ijis.v3i1.37.
A. J. Christy, A. Umamakeswari, L. Priyatharsini, and A. Neyaa, “RFM ranking – An effective approach to customer segmentation,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 10, pp. 1251–1257, 2021, doi: 10.1016/j.jksuci.2018.09.004.
A. T. Wiratama, “Analisis Segmentasi Pelanggan Dengan RFM Model Pada Pt . Arthamas Citra Mandiri Menggunakan Metode Fuzzy C-Means Clustering,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 2, pp. 1986–1993, 2019.
R. Y. Firmansah, J. Dedy Irawan, and N. Vendyansyah, “Analisis Rfm (Recency, Frequency and Monetary) Produk Menggunakan Metode K-Means,” JATI (Jurnal Mhs. Tek. Inform., vol. 5, no. 1, pp. 334–341, 2021, doi: 10.36040/jati.v5i1.3282.
A. P. Pramudiansyah, “Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary,” J. Ilm. Ilmu Komput., vol. 7, no. 2, pp. 06–19, 2021, doi: 10.35329/jiik.v7i2.201.
N. W. Wardani and E. Hartono, “Identifikasi Karakter Pelanggan Retail Berpotensi Churn Dengan Metoda Recency, Frequency Dan Monetary,” J. Teknol. Inf. dan …, pp. 268–277, 2020, [Online]. Available: https://www.jurnal.undhirabali.ac.id/index.php/jutik/article/view/1155
I. Rahma, P. Prima Arhandi, and A. Tufika Firdausi, “Penerapa Metode Hierarchical Clustering Dan K-Means Clustering Untuk Mengelompokkan Potensi Lokasi Penjualan Linkaja,” J. Inform. Polinema, vol. 6, no. 1, pp. 15–22, 2020, doi: 10.33795/jip.v6i1.287.
