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Penerapan Algoritma K-Medoids Untuk Menentukan Segmentasi Pelanggan
Abstract
Abstrak
Strategi pemasaran berorientasi pelanggan memiliki peranan penting dalam mengelola hubungan baik dengan pelanggan. Agar strategi pemasaran tepat sasaran, segmentasi pelanggan dapat digunakan untuk mengelompokkan pelanggan berdasarkan karakteristik yang sama. Dalam penyusunan strategi pemasaran dapat memanfaatkan TI di bidang komputasi, salah satunya adalah data mining. Pemanfaatan teknologi komputasi untuk pengolahan data yang belum maksimal mengakibatkan penumpukan data yang miskin informasi. Pada penelitian ini dilakukan penerapan teknik clustering dengan menggunakan algoritma K-Medoids pada dataset transaksi penjualan untuk menentukan segmentasi pelanggan. Penyusunan strategi pemasaran ditentukan berdasarkan tipe dan karakteristik pelanggan pada setiap cluster atau segmen pelanggan yang terbentuk. Uji validitas cluster menggunakan Silhouette Index dan Davies Boulbin Index dilakukan untuk menentukan jumlah cluster yang paling optimal. Hasil penelitian ini menunjukan bahwa jumlah cluster optimal adalah 3 (tiga) cluster dengan nilai maksimum Silhouette Index adalah 0,375 dan nilai minimum Davies Doulbin Index adalah 1,030. Segmen pelanggan hasil penelitian adalah lost customer, core customer, dan new customer.
Kata kunci: algoritma k-medoids, clustering, data mining, segmentasi pelanggan, strategi pemasaran
Abstract
Customer-oriented marketing strategies play an important role in managing good relationships with customers. To keep marketing strategies on target, customer segmentation can be used to group customers based on the same characteristics. In the preparation of marketing strategies can utilize IT in the field of computing, one of which is data mining. The utilization of computing technology for data processing that has not been maximized resulted in a poor accumulation of information data. In this study, the application of clustering techniques using the K-Medoids algorithm on sales transaction dataset to determine customer segmentation. The preparation of a marketing strategy is determined based on the characteristics and types of customers in each cluster or segment of customers formed. cluster validity tests using the Silhouette Index and Davies-Boulbin Index are performed to determine the most optimal number of clusters. The results of this study showed that the optimal number of clusters is 3 (three) clusters with a maximum silhouette index value of 0.375 and the minimum value of the davies-bouldin index is 1.030. The customer segments of the research results are lost customers, core customers, and new customers.
Keywords: k-medoids algorithm, clustering, data mining, customer segmentation, marketing strategyFull Text:
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B. E. Adiana, I. Soesanti, and A. E. Permanasari, “Analisis Segmentasi Pelanggan Menggunakan Kombinasi Rfm Model Dan Teknik Clustering,” J. Terap. Teknol. Inf., vol. 2, no. 1, pp. 23–32, 2018, doi: 10.21460/jutei.2018.21.76.
R. W. Sembiring Brahmana, F. A. Mohammed, and K. Chairuang, “Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 11, no. 1, pp. 32–43, 2020, doi: 10.24843/lkjiti.2020.v11.i01.p04.
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., 2018, doi: 10.1016/j.jksuci.2018.09.004.
A. R. Riszky and M. Sadikin, “Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan,” J. Teknol. dan Sist. Komput., vol. 7, no. 3, pp. 103–108, 2019, doi: 10.14710/jtsiskom.7.3.2019.103-108.
W. A. Triyanto, “Algoritma K-Medoids Untuk Penentuan Strategi Pemasaran Produk,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 6, no. 1, pp. 183–188, 2015, doi: 10.24176/simet.v6i1.254.
I. Kamila, U. Khairunnisa, and M. Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 1, pp. 119–125, 2019, doi: 10.24014/rmsi.v5i1.7381.
R. Danar Dana, C. Lukman Rohmat, and A. Rizki Rinaldi, “Strategi Marketing Penerimaan Mahasiswa Baru Menggunakan Machine Learning dengan Teknik Clustering,” J. Inform. J. Pengemb. IT, vol. 04, no. 2, pp. 201–204, 2019, doi: 10.30591/jpit.v4i2-2.1879.
C. Astria, A. P. Windarto, and D. Hartama, “Penerapan K-Medoid Pada Rumah Tangga Yang Memiliki Sumber Penerangan Listrik Pln Berdasarkan Provinsi,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 604–609, 2019, doi: 10.30865/komik.v3i1.1667.
P. Arora, Deepali, and S. Varshney, “Analysis of K-Means and K-Medoids Algorithm for Big Data,” Phys. Procedia, vol. 78, no. December 2015, pp. 507–512, 2016, doi: 10.1016/j.procs.2016.02.095.
S. Harikumar and P. V. Surya, “K-Medoid Clustering for Heterogeneous DataSets,” Procedia Comput. Sci., vol. 70, pp. 226–237, 2015, doi: 10.1016/j.procs.2015.10.077.
D. Triyansyah and D. Fitrianah, “Analisis Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing,” J. Telekomun. dan Komput., vol. 8, no. 3, pp. 163–182, 2018, doi: 10.22441/incomtech.v8i3.4174.
A. D. Andini and T. Arifin, “Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung,” J. RESPONSIF Ris. Sains …, vol. 2, no. 2, pp. 128–138, 2020, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti/article/view/247.
D. Marlina, N. Lina, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 4, no. 2, pp. 64–71, 2018, doi: 10.24014/coreit.v4i2.4498.
H. Astuti, “Penerapan Data Mining Menggunakan Metode K-Means Clustering Untuk Pengelompokkan Data Pelanggan (Studi Kasus: PT. Pinus Merah Abadi),” J. Web Inform. Teknol., vol. 4, no. 1, pp. 9–16, 2019.
M. Aryuni, E. Didik Madyatmadja, and E. Miranda, “Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering,” Proc. 2018 Int. Conf. Inf. Manag. Technol. ICIMTech 2018, no. September, pp. 412–416, 2018, doi: 10.1109/ICIMTech.2018.8528086.
M. Kadir and A. Achyar, “Customer Segmentation on Online Retail using RFM Analysis: Big Data Case of Bukku.id,” ICEASD, 2019, doi: 10.4108/eai.1-4-2019.2287279.
F. Hadi, M. Mustakim, D. O. Rahmadia, F. H. Nugraha, N. P. Bulan, and S. Monalisa, “Penerapan K-Means Clustering Berdasarkan RFM Mofek Sebagai Pemetaan dan Pendukung Strategi Pengelolaan Pelanggan (Studi Kasus: PT. Herbal Penawar Alwahidah Indonesia Pekanbaru),” J. Sains dan Teknol. Ind., vol. 15, no. 1, pp. 69–76, 2017.
S. Monalisa, “Segmentasi Perilaku Pembelian Pelanggan Berdasarkan Model RFM dengan Metode K-Means,” QUERY J. Sist. Inf., vol. 02, no. April, pp. 9–15, 2018.
W. S. Manjoro, M. Dhakar, and B. K. Chaurasia, “Operational analysis of k-medoids and k-means algorithms on noisy data,” Int. Conf. Commun. Signal Process. ICCSP 2016, pp. 1500–1505, 2016, doi: 10.1109/ICCSP.2016.7754408.
N. Arbin, N. S. Suhaimi, N. Z. Mokhtar, and Z. Othman, “Comparative analysis between k-means and k-medoids for statistical clustering,” Proc. - AIMS 2015, 3rd Int. Conf. Artif. Intell. Model. Simul., pp. 117–121, 2016, doi: 10.1109/AIMS.2015.82.
S. Defiyanti, M. Jajuli, and N. Rohmawati, “K-Medoid Algorithm in Clustering Student Scholarship Applicants,” Sci. J. Informatics, vol. 4, no. 1, pp. 27–33, 2017, doi: 10.15294/sji.v4i1.8212.
G. Ogbuabor and U. F. N, “Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value,” Int. J. Comput. Sci. Inf. Technol., vol. 10, no. 2, pp. 27–37, 2018, doi: 10.5121/ijcsit.2018.10203.
A. K. Singh, S. Mittal, P. Malhotra, and Y. V. Srivastava, “Clustering Evaluation by Davies-Bouldin Index(DBI) in Cereal data using K-Means,” Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020, no. Iccmc, pp. 306–310, 2020, doi: 10.1109/ICCMC48092.2020.ICCMC-00057.
D. C. Li, W. L. Dai, and W. T. Tseng, “A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business,” Expert Syst. Appl., vol. 38, no. 6, pp. 7186–7191, 2011, doi: 10.1016/j.eswa.2010.12.041.
C. D. Rumiarti and I. Budi, “Customer Segmentation for Customer Relationship Management on Retail Company: Case Study PT Gramedia Asri Media,” J. Sist. Inf., vol. 13, no. 1, pp. 1–10, 2017, doi: 10.21609/jsi.v13i1.525.
DOI: https://doi.org/10.32520/stmsi.v10i3.1332
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