ANALYSIS OF CUSTOMER DATA IN SELECTING POTENTIAL CUSTOMERS USING DATA MINING WITH THE K-MEANS ALGORITHM

Randi Rian Putra, Sri Handayani, Fahmi Kurniawan, Cendra Wadisman

Abstract


Abstract:  In an increasingly competitive business era, understanding customer characteristics and preferences has become essential. This research aims to analyze customer data to find potential customers using data mining techniques and the K-Means algorithm. The data used comes from the company's database which includes transaction history, demographics and customer interactions. The K-Means algorithm is applied to cluster customer data into several groups based on similar characteristics. The clustering results show that there are customer segments with different profit potential for the company. By identifying potential customer segments, companies can design more targeted marketing strategies, increase the efficiency of allocating resources, and ultimately increase ROI (Return on Investment). This study provides guidance for companies in optimizing their approach to reaching potential customers and achieving marketing success.

Keywords: Data Mining, K-Means Algorithm, Customers, Data Analysis, Potential Customers, Marketing Strategy


Full Text:

PDF

References


Ahsina, N., Fatimah, F., & Rachmawati, F. (2022). Analisis Segmentasi Pelanggan Bank Berdasarkan Pengambilan Kredit Dengan Menggunakan Metode K-Means Clustering. Jurnal Ilmiah Teknologi Infomasi Terapan, 8(3). https://doi.org/10.33197/jitter.vol8.iss3.2022.883

Andi Cuhwanto, Y. N., & R, D. A. (2021). Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means. Petir, 15(1), 48–56. https://doi.org/10.33322/petir.v15i1.1358

Murpratiwi, S. I., Agung Indrawan, I. G., & Aranta, A. (2021). Analisis Pemilihan Cluster Optimal Dalam Segmentasi Pelanggan Toko Retail. Jurnal Pendidikan Teknologi Dan Kejuruan, 18(2), 152. https://doi.org/10.23887/jptk-undiksha.v18i2.37426

Muzaqi, K. A., Junaidi, A., & Saputra, W. A. (2022). Klasifikasi Status Gizi Pada Lansia Menggunakan Learning Vector Quantization 3 (LVQ 3). Journal of Dinda : Data Science, Information Technology, and Data Analytics, 2(1), 28–36. https://doi.org/10.20895/dinda.v2i1.272

Putra, Randi Rian & nadya, andhika putri. (2022). Implementasi sistem informasi perpustakaan dalam meningkatkan pelayanan dan struktur perpustakaan pada smp swasta pab 9 1. Jaringan Sistem Informasi …, 6(1), 83–88. http://ojsamik.amikmitragama.ac.id/index.php/js/article/view/136

Putra, R. R., Putri, N. A., & Wadisman, C. (2022). Village Fund Allocation Information System for Community Empowerment in Klambir Lima Kebun Village. Journal of Applied …, 3(2), 98–104. https://journal.yrpipku.com/index.php/jaets/article/view/681%0Ahttps://journal.yrpipku.com/index.php/jaets/article/download/681/467

Rianto, M., Rusdiah, R., & Ichwan, H. (2022). Penerapan Data Mining Dengan Metode Naïve Bayes Dan Learning Vector Quantization Credit Rating Dalam Memprediksi Kelayakan Pemberian Kredit Oleh PT. BPR Lebak Sejahtera. Respati, 17(1), 69. https://doi.org/10.35842/jtir.v17i1.443

Rofianto, D., Arifin, O., & Widyawati, D. K. (2023). Perbandingan Metode Klasifikasi Learning Vector Quantization Dengan Diskriminan Fisher Pada Data Bunga Iris. 7(1), 44–49.

Romadhona, W., Indarmawan Nugroho, B., & Alim Murtopo, A. (2022). Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means. Jurnal Minfo Polgan, 11(2), 100–104. https://doi.org/10.33395/jmp.v11i2.11797

Suarna, N., & Wijaya, Y. A. (2023). ANALISA PENERAPAN METODE CLUSTERING K-MEANS UNTUK PENGELOMPOKAN DATA TRANSAKSI KONSUMEN ( Studi Kasus : Cv . Mitra Indexindo Pratama ). 7(2), 1322–1328.

Sumadikarta, I., & Abeiza, E. (2014). Penerapan Algoritma K-Means Pada Data Mining Untuk Memilih Produk Dan Pelanggan Potensial. Jurnal Satya Informatika, 1, 12–22. https://lppm.usni.ac.id/jurnal/Istiqomah-Sumadikarta-Evan-Abeiza.pdf


Article Metrics

Abstract view : 107 times
PDF – 53 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 PROSIDING UNIVERSITAS DHARMAWANGSA

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Prosiding Universitas Dharmawangsa Terindex pada:

PROSIDING SEMINAR NASIONAL DAN INTERNASIONAL PUBLISHED BY :

UPT. Penerbitan dan Publikasi Ilmiah
UNIVERSITAS DHARMAWANGSA

Alamat : Jl. K. L. Yos Sudarso No. 224 Medan
Kontak : Tel. 061 6635682 - 6613783  Fax. 061 6615190
Surat Elektronik : ppi@dharmawangsa.ac.id

 

 Creative Commons License

Prosiding Seminar Nasional dan Internasional By Universitas Dharmawangsa is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://proceeding.dharmawangsa.ac.id/index.php/PSND/index