Implementation of K-Means Clustering in Online Retail based on Recency, Frequency, and Monetary
Article Sidebar
online shop,
recency frequency monetary,
clustering
Main Article Content
Abstract
During a pandemic like today, many changes have occurred, one of which is the increasing number of online buying and selling sites. Each Online Store offers a variety of products and services with a variety of attractive offers, competing fiercely to attract enthusiasts. With the occurrence of a pattern of change in society, it is necessary to carry out a grouping to obtain information in order to determine a better sales strategy. The grouping process uses techniques from data mining, namely Clustering with the K-Means algorithm based on the Recency Frequency Monetary (RFM) algorithm, it is hoped that by analyzing the three attributes and implementing the K-Means algorithm, it can provide an accurate output and in accordance with the objectives of this study.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
V. No and N. Mona, “Konsep Isolasi Dalam Jaringan Sosial Untuk Meminimalisasi Efek Contagious (Kasus Penyebaran Virus Corona Di Indonesia),†J. Sos. Hum. Terap., vol. 2, no. 2, pp. 117–125, 2020, doi: 10.7454/jsht.v2i2.86.
M. S. Mustafa, M. R. Ramadhan, and A. P. Thenata, “Implementasi Data Mining untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,†Creat. Inf. Technol. J., vol. 4, no. 2, p. 151, 2018, doi: 10.24076/citec.2017v4i2.106.
T. Taufik and E. A. Ayuningtyas, “Dampak Pandemi Covid 19 Terhadap Bisnis Dan Eksistensi Platform Online,†J. Pengemb. Wiraswasta, vol. 22, no. 01, p. 21, 2020, doi: 10.33370/jpw.v22i01.389.
B. Santosa, “Data mining:Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu- Bisnis.Edisi Pertama.,†Data miningTeknik Pemanfaat. Data untuk Keperluan Bisnis. Yogyakarta Graha Ilmu- Bisnis.Edisi Pertama., vol. 33, no. 4, pp. 365–373, 2007.
mohamad jajuli nurul rohmawati, sofi defiyanti, “Implementasi Algoritma K-Means Dalam Pengklasteran Mahasiswa Pelamar Beasiswa,†Jitter 2015, vol. I, no. 2, pp. 62–68, 2015.
F. Nur, M. Zarlis, and B. B. Nasution, “Penerapan Algoritma K-Means Pada Siswa Baru Sekolahmenengah Kejuruan Untuk Clustering Jurusan,†InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 1, no. 2, pp. 100–105, 2017, doi: 10.30743/infotekjar.v1i2.70.
A. K. Wardhani, “Implementasi Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Kajen Pekalongan,†J. Transform., vol. 14, no. 1, pp. 30–37, 2016.
R. A. Asroni, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang,†Ilm. Semesta Tek., vol. 18, no. 1, pp. 76–82, 2015.
A. Bastian, H. Sujadi, and G. Febrianto, “Penerapan Algoritma K-Means Clustering Analysis Pada Penyakit Menular Manusia (Studi Kasus Kabupaten Majalengka),†no. 1, pp. 26–32.
T. Hermanto, “Implementasi Algoritma Association Rule Dan K-Means Sebagai Sistem Rekomendasi Produk Pada Website Penjualan Online,†Stt-Wastukancana.Ac.Id, pp. 70–73.
B. S. Ashari, S. C. Otniel, and Rianto, “Perbandingan Kinerja K-Means Dengan DSCAN Untuk Metode Clustering Data Penjualan Online Retail,†J. Siliwangi, vol. 5, no. 2, pp. 72–77, 2019.