Segmentasi Pelanggan Penjualan Online Menggunakan Metode K-means Clustering

Authors

  • Candra Hafidz Ardana UIN Maulana Malik Ibrahim Malang
  • Adlian Aldita Alif Aisyah Ainur Khoyum Universitas Negeri Semarang
  • Muhammad Faisal UIN Maulana Malik Ibrahim Malang

DOI:

https://doi.org/10.14421/jiska.2024.9.1.1-9

Keywords:

Customer Segmentation, Online Sales, E-Commerce, K-means Clustering, Clustering

Abstract

Customer segmentation is an essential strategy in the online selling industry to understand customer preferences and behavior. This article proposes applying the K-means clustering method in online sales customer segmentation. The method used is the descriptive method. The steps of the research method include literature studies and data processing to be analyzed using the K-means clustering method. The K-means clustering method is then applied to customer data to group it based on relevant attributes. The segmentation results are evaluated and scored using the clustering evaluation metric. The main objective is to explain the use of the K-means clustering method in online sales customer segmentation, focusing on obtaining more profound insights into customer behavior. Efficient customer segmentation allows companies to target customer groups more precisely and efficiently. This article provides practical insights and guidance for e-commerce companies in implementing customer segmentation using K-means clustering to increase efficiency in targeting segmented customers.

References

Angelie, A. V. (2017). Segmentasi Pelanggan Menggunakan Clustering K-Means dan Model RFM (Studi Kasus: PT. Bina Adidaya Surabaya) [Institut Teknologi Sepuluh Nopember]. https://repository.its.ac.id/42240/

Chandra, M. D., Irawan, E., Saragih, I. S., Windarto, A. P., & Suhendro, D. (2021). Penerapan Algoritma K-Means dalam Mengelompokkan Balita yang Mengalami Gizi Buruk Menurut Provinsi. BIOS : Jurnal Teknologi Informasi Dan Rekayasa Komputer, 2(1), 30–38. https://doi.org/10.37148/bios.v2i1.19

Chen, D. (2015). Online Retail. UCI Machine Learning Repository.

Deng, Y. (2023). Specific Strategies for Innovating Marketing Models of E-commerce Enterprises in the Internet Era. Academic Journal of Business & Management, 5(13), 22–26. https://doi.org/10.25236/AJBM.2023.051304

Dewa, F. A., & Jatipaningrum, M. T. (2019). Segmentasi E-Commerce dengan Cluster K-Means dan Fuzzy C-Means. Jurnal Statistika Industri Dan Komputasi, 4(01), 53–67. https://doi.org/10.34151/STATISTIKA.V4I01.1054

Duo, J., Zhang, P., & Hao, L. (2021). A K-means Text Clustering Algorithm Based on Subject Feature Vector. Journal of Web Engineering, 20(6), 1935-1946–1935–1946. https://doi.org/10.13052/jwe1540-9589.20612

Febrianti, F., & Beni, S. (2023). Strategi Mempertahankan Loyalitas Pelanggan pada Usaha Kuliner di Kecamatan Bengkayang. Inovasi Pembangunan : Jurnal Kelitbangan, 11(02), 189–210. https://doi.org/10.35450/jip.v11i02.384

Ghosh, S., & Kumar, S. (2013). Comparative Analysis of K-Means and Fuzzy C-Means Algorithms. International Journal of Advanced Computer Science and Applications, 4(4). https://doi.org/10.14569/IJACSA.2013.040406

Gupta, G. K., Agrawal, D., Singh, R. K., & Arya, R. K. (2013). Prevalence, Risk Factors and Socio Demographic Co-Relates of Adolescent Hypertension in District Ghaziabad. Indian Journal of Community Health, 25(3), 293–298. https://www.iapsmupuk.org/journal/index.php/IJCH/article/view/331

Istiana, M. I. (2013). Segmentasi Pelanggan Menggunakan Algoritma K-Means Sebagai Dasar Strategi Pemasaran pada LAROIBA Seluler Oleh: Mike Indra Istiana [Universitas Dian Nuswantoro Semarang]. http://eprints.dinus.ac.id/12733/2/abstrak_12903.pdf

Kurniawati, I. Y. (2018). Segmentasi Pelanggan Menggunakan Clustering K-Means [Universitas 17 Agustus 1945]. http://repository.untag-sby.ac.id/868/

Bangoria, B., Mankad, N., & Pambhar, V. (2013). A survey on Efficient Enhanced K-Means Clustering Algorithm. International Journal for Scientific Research and Development, 1(9), 1756–1758. https://doi.org/10.2/JQUERY.MIN.JS

Nawangsih, I. (2023). Analisa Penjualan Produk Kosmetik Dengan Metode Algoritma K-Means Di Toko Erremy. Bulletin of Information Technology (BIT), 4(1), 140–145. https://doi.org/10.47065/bit.v4i1.468

Perdana, S. A., Florentin, S. F., & Santoso, A. (2022). Analisis Segmentasi Pelanggan Menggunakan K-Means Clustering Studi Kasus Aplikasi Alfagift. Sebatik, 26(2), 446–457. https://doi.org/10.46984/sebatik.v26i2.1991

Savitri, A. D., Bachtiar, F. A., & Setyawan, N. Y. (2018). Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus: Belle Crown Malang). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(9), 2957–2966. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/2489

Brahmana, R. W. S., Mohammed, F. A., & Chairuang, K. (2020). Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 11(1), 32. https://doi.org/10.24843/LKJITI.2020.v11.i01.p04

Zhang, Z., Ni, G., & Xu, Y. (2020). Comparison of Trajectory Clustering Methods based on K-means and DBSCAN. Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020, 557–561. https://doi.org/10.1109/ICIBA50161.2020.9277214

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Published

2024-01-25

How to Cite

Ardana, C. H., Khoyum, A. A. A. A. A., & Faisal, M. (2024). Segmentasi Pelanggan Penjualan Online Menggunakan Metode K-means Clustering. JISKA (Jurnal Informatika Sunan Kalijaga), 9(1), 1–9. https://doi.org/10.14421/jiska.2024.9.1.1-9