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CUSTOMER PROFILLING FOR PRECISION MARKETING USING RFM METHOD, K-MEANS ALGORITHM AND DECISION TREE
Sularso Budilaksono(a*), Jupriyanto(b*), Dedi Dwi Saputra(c*) M. Anno Suwarno(d*), I Gede Agus Suwartane(e*), Andri Agung Riyadi(f*), Lukman Azhari (g*), Achmad Fauzi (h*), Mahpud (i*), Novita Mariana (j*), Maya Syafriana Effendi (k*)

(a) Informatics Department, University of Persada Indonesia YAI
Jalan Diponegoro 74 Jakarta
*sularso[at]upi-yai.ac.id
(b) Magister Ilmu Komputer, STMIK Nusa Mandiri
Jalan Kramat Raya 18 Jakarta
*jupriyanto.kahar[at]gmail.com
(c) Magister Ilmu Komputer, STMIK Nusa Mandiri
Jalan Kramat Raya 18 Jakarta
*14002190[at]nusamandiri.ac.id
(d) Informatics Department, University of Persada Indonesia YAI
Jalan Diponegoro 74 Jakarta
*suwarno[at]upi-yai.ac.id
(e) Informatics Department, University of Persada Indonesia YAI
Jalan Diponegoro 74 Jakarta
*agus.suwartane[at]gmail.com
(f) Magister Ilmu Komputer, STMIK Nusa Mandiri
Jalan Kramat Raya 18 Jakarta
*andriagu1603[at]nusamandiri.ac.id
(g) Teknik informatika, Universitas Muhammadiyah Tangeng
Jalan Perintis Kemerdekaan I No.1, Babakan, Kec. Tangerang, Kota Tangerang, Banten
*lukmanazhari85[at]gmail.com
(h) Teknik informatika, Universitas Muhammadiyah Tangeng
Jalan Perintis Kemerdekaan I No.1, Babakan, Kec. Tangerang, Kota Tangerang, Banten
*ac.fauzi25[at]yahoo.com
(i) Teknik informatika, Universitas Muhammadiyah Tangeng
Jalan Perintis Kemerdekaan I No.1, Babakan, Kec. Tangerang, Kota Tangerang, Banten
*mahpud18[at]gmail.com
(j) Sistem Informasi, Universitas Stikubank Semarang
Jl. Tri Lomba Juang no 1 Semarang
*novita_mariana[at]edu.unisbank.ac.id
(k) Manajemen, Universitas Persada Indonesia YAI
Jl. Diponegoro 74 Jakarta
*mayasyafriana[at]gmail.com




Abstract

Precision marketing is the companys ability to offer products specifically made to customers. This decision can give the company the ability to attract customers to always buy continuously. This study presents a trend model for accurately predicting monthly supply quantities / The method used in the first stage is the RFM (Recency, Frequency, Monetary) method for selecting attributes to group customers into different groups. The output of the first stage is clustered using the K-Means Algorithm. The output of clustering is then classified using the Decision Tree and compared with the K Nearest Neighbor method. The dataset that is processed is sales data from Syifamart As-Syifa Boarding School in Subang with 351,158 rows of data. The clustering process produces 4 optimal clusters. The four clusters are then classified using the Decision Tree algorithm to determine the potential and non-potential characteristics of each customer.

Keywords: Precision Marketing; Customer Profiling; RFM (Recency, Frequency, Monetary); K-Means Algorithm; Decision Tree Algorithm, K Nearest Neighbor Algorithm.

Topic: International Symposium of Engineering, Technology, and Health Sciences

Link: https://ifory.id/abstract/zfaUEwtATyMK

Conference: The 3rd International Conference on Sustainability and Innovation (ICoSI 2019)

Plain Format | Corresponding Author (Sularso Budilaksono)

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