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All Journal JUSIFO : Jurnal Sistem Informasi 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 Journal of Computer System and Informatics (JoSYC) Indonesian Journal of Informatic Research and Software Engineering METRIK SERIAL TEKNOLOGI DAN SAINS
Zarnelly Zarnelly
Jurusan Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Physics Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Penerapan Algoritma Fuzzy C-Means untuk Klasterisasi Customer Lifetime Value menggunakan Model LRFMD Ramadhani, Indah; Afdal, M; Mustakim, Mustakim; Zarnelly, Zarnelly
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7613

Abstract

PT X is a retail company engaged in printing. The company has not differentiated between information about profitable and unprofitable customers for the company. Transaction data is only used as profit and loss information so they do not know the characteristics of the customers they have. In addition, the lack of extensive services in the merchandise category is one of the reasons the company's revenue has not reached the predetermined target. Currently, the company has opened additional services in the merchandise field. This research aims to identify customer segmentation as well as analyze the characteristics and provide a strategy proposal that will be submitted to PT. X. Customer loyalty and characteristics have a significant impact on a company. To identify customers who show loyalty to the company, the Fuzzy C-Means algorithm is used to perform clustering, using the Davies Bouldin Index (DBI) to evaluate the clustering results. The model used is in accordance with the principles of Length, Recency, Frequency, Monetary and Diversity (LRFMD) to categorize purchasing patterns. By analyzing LRFMD variables, it is possible to identify customers who are loyal to the company and those who are not. This research produces 6 clusters with the best cluster or supestar customer in cluster 6, the second best value customer or golden customer is cluster 2, the average value customer or typical customer is cluster 4 and 5 and the lowest cluster or dormant customer is in cluster 3.
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 Ahsyar, Tengku Khairil Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Anahyu, Yelfi Dwi Anofrizen Anofrizen fitriano, ernesto Hamzah, Muhammad Luthfi Inggih Permana M Afdal Megawati , Megawati Megawati - Mustakim Mustakim Muttakin, Fitriani Pristiawati, Andani Putri Putra, Ahdika Sawiyya Ramadhani, Indah Razmi, Fikri Rice Novita Rozanda, Nesdi Evrilyan Siti Monalisa Suwito, Bagus Anjari Syaifuddin, Aditya Ula, Walid Alma