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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JUSIFO : Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Sistem Cerdas JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JoMMiT : Jurnal Multi Media dan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science
Ahsyar, Tengku Khairil
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan Afifah, Tiara Afrah; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7605

Abstract

In implementing customer relationship management, there are still many companies that have not utilized CRM optimally as part of their business strategy. As is the case with UD Sandeni. UD Sandeni still has problems in managing its relationships with customers because UD Sandeni does not fully understand the difference between customer information that is profitable and unprofitable for the company's sustainability. UD Sandeni has used a system to manage customer transaction data. However, this system is only used to calculate profits and create bookkeeping for registered agents so that UD Sandeni does not have an in-depth understanding of the characteristics of its customers. To overcome this problem, the solution that can be applied is to use customer grouping techniques, such as clustering. Customer transaction data is processed using a clustering process with K-Means and LRFM. Test the validity of cluster results using DBI and calculate CLV values using AHP weights to produce cluster rankings. The results of this research obtained customer clustering which consists of 2 segments, namely cluster 1 which has the highest CLV value of 0.3171156 with a total of 298 customers and includes the High Value Loyal Customers segmentation, and cluster 2 with a CLV value of 0.1434054 with a total of 72 customers. which is included in the segmentation of uncertain new customers (uncertain lost customers).
Co-Authors Afifah, Tiara Afrah Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Alriwanda, A Angraini Angraini Angraini Ayulya, Agisti Mutiara Eki Saputra Febi Nur Salisah, Febi Nur Hamzah, Muhammad Lutfi Hamzah, Muhammad Luthfi Idria Maita Irfan Faruqi, Nouval Jazman, Muhammad Kusuma, Muhammad Wira Ade MaySarah, M Megawati Megawati, M Mona Fronita, Mona Ningsih, Widiah Nurhadi, Muhammad Nurrahma, Intan Rahmad Fauzi Rahmawita, Medyantiwi Rice Novita Siti Monalisa Syaifullah Syaifullah Zarnelly Zarnelly