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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Query : Jurnal Sistem Informasi
Siti Monalisa
UIN Suska Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Sistem Rekomendasi Produk Menggunakan Model RFM, AHP dan Ranked Clustering Siti Monalisa; Achmad Harpin Asrori; Fitra Kurnia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.186 KB) | DOI: 10.29207/resti.v3i2.810

Abstract

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.
Co-Authors Achmad Harpin Asrori Amelia Septia Roza E. Denni Prima Putra Eghi Ditendra Fakhri Hadi Fitra Kurnia Imelda Erza Raynaldi Setiawan Stedico Anderjovi Surya Lesmana