Sistem Rekomendasi Produk Menggunakan Model RFM, AHP dan Ranked Clustering

  • Siti Monalisa UIN Suska Riau
  • Achmad Harpin Asrori UIN Suska Riau
  • Fitra Kurnia UIN Suska Riau
Keywords: AHP, FCM, Competition, Recommendations, RFM, Convection Business


Monstreation is a business engaged in clothing convection, these business products are marketed online such as jackets and shirts for class, shirt and community clothing. The problem that occurs in this convection is the lack of product recommendation services to customers. Another problem is that if there are customers who order products that are not in accordance with their needs, the customer will rarely order products at Monstreation. The solution used is to provide services that match the characteristics of the customer, for example by giving product recommendations. Product recommendations are also needed considering this type of business is a business that has many business rivals. The steps taken in this study begin by collecting customer transaction data, then the data is transformed into RFM criteria data. After being transformed, the data is weighted using AHP, after that the RFM data is weighted then grouped / clustered. The grouping results are validated with DBI. From the experiments conducted it is known that the number of cluster 3 is the optimal number of clusters in product grouping. After it is ranked based on the value of the total weight. From the experiments conducted, it is known that the results of the 3 customer clusters, the customers who have the highest weight value are customers in cluster 1. The results of this study are a product recommendation that is an association of product history of customers who have a cluster similarity and a product recommendation information system.



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[1] Ngai EWT, Xiu L, Chau DCK. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications [Internet]. 2009;36(2 PART 2):2592–602. Available from:
[2] Khajvand M, Zolfaghar K, Ashoori S, Alizadeh S. Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science [Internet]. 2011;3:57–63. Available from:
[3] Buttle F, Stan M. Customer Relationship Management. Third edit. Butterworth-Heinemann; 2015.
[4] Arthur M H. Strategic database marketing. Chicago: Probus Publishing Co. 1994.
[5] Thomas JCR, Peñas MS, Mora M. New Version of Davies-Bouldin Index for Clustering Validation Based on Cylindrical Distance. In: Proceedings - International Conference of the Chilean Computer Science Society, SCCC. 2017.
[6] Ramadhan A, Efendi Z. Perbandingan K-Means dan Fuzzy C-Means untuk Pengelompokan Data User Knowledge Modeling. 2017;18–9.
[7] Parvaneh A, Abbasimehr H, Tarokh MJ. Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value. Journal of Optimization in Industrial Engineering. 2012;5(11):25–31.
[8] Kurniawaty Dita. Dkk., 2014. Rekomendasi Produk Berdasarkan Loyalitas Pelanggan Menggunakan Integrasi Metode AHP Dan Teknik Penggalian Data : Studi Kasus CV.XYZ. Inovasi dalam Desain dan Teknologi Seminar Nasional Sistem Informasi Indonesia.
[9] Triyanto Wiwit Agus., 2014. Association Rule Mining Untuk Penentuan Rekomendasi Promosi Produk. Jurnal SIMETRIS, Vol 5 No 2.
[10] Shabir Fadil dan Abdul Rachman M., 2016. Rekomendasi Pembelian Personal Komputer Dengan Metode Ranked Clustering. Jurnal Ilmiah ILKOM Volume 8 Nomor 2.
[11] Thorleuchter D, Poel D Van Den, Prinzie A. Expert Systems with Applications Analyzing existing customers ’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing.
[12] Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques [Internet]. San Francisco, CA, itd: Morgan Kaufmann. 2012. 745
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