DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.78-83

Segmentasi pelanggan menggunakan algoritme bisecting k-means berdasarkan model recency, frequency, dan monetary (RFM)

Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Department of Informatics, Universitas Mulawarman, Indonesia

Received: 5 Mar 2019; Revised: 14 Nov 2019; Accepted: 18 Nov 2019; Available online: 5 Feb 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.

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Subject bisecting k-means; customer segmentation; RFM; best cluster; silhouette coefficient
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Keywords: bisecting k-means; customer segmentation; RFM; best cluster; silhouette coefficient
Funding: Universitas Mulawarman, Indonesia; PT. Dinar Energi Utama

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Section: Original Research Articles
Language : ID
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  1. A. Ali, P. D. Paramita, and A. Fathoni, "Analisis faktor-faktor yang mempengaruhi kepuasan pelanggan di perusahaan galangan kapal (studi kasus pada PT. Janata Marina Indah Semarang)," Journal of Management, vol. 2, no. 2, pp. 1-12, 2016
  2. T. Hardiani and R. Hartanto, "Segmentasi nasabah tabungan menggunakan model RFM (recency, frequency, monetary) dan k-means pada lembaga keuangan mikro," in Seminar Nasional Teknologi Informasi dan Komunikasi Terapan (SEMATIK), Semarang, Indonesia, Nov. 2015, pp. 463-468
  3. B. E. Adiana, I. Soesanti, and A. E. Permanasari, "Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering," Jurnal Terapan Teknologi Informasi, vol. 2, no. 1, pp. 23-32, 2018. doi: 10.21460/jutei.2018.21.76
  4. M. A. Yaqin, S. Naja, S. K. Al-Azhar, and K. Mahbullah, "Penerapan metode fuzzy sugeno pada analisis RFM untuk menentukan indeks produk pada permainan hayday," Prosiding SENIATI, vol. 5, no. 1, pp. 50-56, 2019
  5. J. T. Wei, S.-Y. Lin, Y.-Z. Yang, and H.-H. Wu, "Applying data mining and RFM model to analyze customers' values of a veterinary hospital," in 2016 International Symposium on Computer, Consumer and Control (IS3C), Xian, China, Jul. 2016, pp. 481-484. doi: 10.1109/IS3C.2016.126
  6. N. P. P. Yuliari, I. K. G. D. Putra, and N. K. D. Rusjayanti, "Customer segmentation through fuzzy c-means and fuzzy RFM method," Journal of Theoretical and Applied Information Technology, vol. 78, no. 3, pp. 380-385, 2015
  7. J. Han, M. Kamber, and J. Pei, Data mining: concepts and techniques, 3rd edition. MA: Morgan Kaufman Publishers, 2012
  8. C. Slamet, A. Rahman, M. A. Ramdhani, and W. Darmalaksana, "Clustering the verses of the Holy Qur'an using k-means algorithm," Asian Journal of Information Technology, vol. 15, no. 24, pp. 5159-5162, 2016
  9. A. P. Windarto, "Penerapan data mining pada ekspor buah-buahan menurut negara tujuan menggunakan k-means clustering method," Techno. Com, vol. 16, no. 4, pp. 348-357, 2017. doi: 10.33633/tc.v16i4.1447
  10. A. Bastian, "Penerapan algoritma k-means clustering analysis pada penyakit menular manusia (studi kasus kabupaten Majalengka)," Jurnal Sistem Informasi, vol. 14, no. 1, pp. 28-34, 2018
  11. P. Purnawansyah, H. Haviluddin, A. F. O. Gafar, and I. Tahyudin, "Comparison between k-means and fuzzy c-means clustering in network traffic activities," in International Conference on Management Science and Engineering Management, Kanazawa, Japan, Jul. 2017, pp. 300-310. doi: 10.1007/978-3-319-59280-0_24
  12. T. Hardiani, “Segmentasi nasabah simpanan menggunakan fuzzy c means dan fuzzy rfm (recency, frequency, monetary) pada BMT xyz,” Network Engineering Research Operation (NERO), vol. 3, no. 3, pp. 185-192, 2018
  13. N. Puspitasari, R. Rosmasari, and S. Stefanie, "Penentuan prioritas perbaikan jalan menggunakan fuzzy c-means: studi kasus perbaikan jalan di kota Samarinda," Jurnal Teknologi dan Sistem Komputer, vol. 5, no. 1, pp. 7-14, 2017. doi: 10.14710/jtsiskom.5.1.2017.7-14
  14. M. N. Sutoyo and A. T. Sumpala, "Penerapan fuzzy c-means untuk deteksi dini kemampuan penalaran matematis," Scientific Journal of Informatics, vol. 2, no. 2, pp. 129-135, 2016. doi: 10.15294/sji.v2i2.5080
  15. R. J. E. Putra, N. Nasution, and Y. Yummastian, "Aplikasi e-zakat penerimaan dan penyaluran menggunakan fuzzy c-means (studi kasus: LAZISMU Pekanbaru)," Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, vol. 6, no. 2, pp. 42-54, 2015
  16. A. A. Rachman and Z. Rustam, "Cancer classification using fuzzy c-means with feature selection," in 12th International Conference Mathematics, Statistics, and Their Applications, Banda Aceh, Indonesia, Oct. 2016, pp. 31-34. doi: 10.1109/ICMSA.2016.7954302
  17. N. Puspitasari, J. A. Widians, and P. Pohny, "A clustering of generative and infectious diseases using fuzzy c-means," ITSMART: Jurnal Teknologi dan Informasi, vol. 7, no. 1, pp. 22-28, 2018
  18. R. Patil and A. Khan, "Bisecting K-means for clustering web log data," International Journal of Computer Applications, vol. 116, no. 19, pp. 36-41, 2015. doi: 10.5120/20448-2799
  19. Q. Zhang, Y. Chi, and N. He, "Color image segmentation based on a modified k-means algorithm," in 7th International Conference on Internet Multimedia Computing and Service, New York, USA, Aug. 2015, pp. 1-4. doi: 10.1145/2808492.2808538
  20. M. S. G. Karypis, V. Kumar, and M. Steinbach, "A comparison of document clustering techniques," in KDD Workshop on Text Mining, Boston, USA, Aug. 2000, pp. 1-20
  21. M. Wati, W. Indrawan, J. A. Widians, and N. Puspitasari, "Data mining for predicting students' learning result," in 4th International Conference on Computer Applications and Information Processing Technology, Bali, Indonesia, Aug. 2017, pp. 1-4. doi: 10.1109/CAIPT.2017.8320666
  22. P. Chapman et al., CRISP-DM 1.0 Step-by-step data mining guide. CRISP-DM consortium, 2000
  23. C.-H. Cheng and Y.-S. Chen, "Classifying the segmentation of customer value via RFM model and RS theory," Expert systems with applications, vol. 36, no. 3, pp. 4176-4184, 2009. doi: 10.1016/j.eswa.2008.04.003
  24. P. Tan, M. Steinbach, and V. Kumar, "Introduction to Data Mining, Boston: Person Education," ed: Inc, 2006
  25. E. Prasetyo, Data mining mengolah data menjadi informasi menggunakan Matlab. Yogyakarta: Andi Offset, 2014
  26. L. Kaufman and P. J. Rousseuw, Finding groups in data. New York: John Wiley & Sons, 1990
  27. Y. K. Jain and S. K. Bhandare, "Min max normalization based data perturbation method for privacy protection," International Journal of Computer & Communication Technology, vol. 2, no. 8, pp. 45-50, 2011

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