ALGORITMA K-MEANS UNTUK PENGELOMPOKAN PERILAKU CUSTOMER

  • Harun Mukhtar Fakultas Ilmu Komputer, Universitas Muhammadiyah Riau
  • Ilham Dwi Pramaditya Fakultas Ilmu Komputer, Universitas Muhammadiyah Riau
  • Wahyu Saputra Weisdiyanto Fakultas Ilmu Komputer, Universitas Muhammadiyah Riau
  • Saddam Hardian_Putra Fakultas Ilmu Komputer, Universitas Muhammadiyah Riau
  • Diana Trimuawasih Fakultas Ilmu Komputer, Universitas Muhammadiyah Riau
  • Azzahra Auralia Rilda Fakultas Ilmu Komputer, Universitas Muhammadiyah Riau
Keywords: K-Means, Customer Behavior Analysist, Big Data Analytics in Marketing, Customer Segmentation, Data Mining in Marketing

Abstract

In the rapidly evolving digital era, understanding customer purchasing behavior is crucial for marketing strategies and business development. This study uses the K-means clustering algorithm to analyze and segment customer purchasing behavior. This algorithm effectively partitions data into groups based on similar characteristics. The aim of this study is to identify purchasing behavior patterns using attributes such as purchase frequency, expenditure amount, and product types. By segmenting customers into homogeneous groups, companies can design more effective marketing strategies and better personalization. The results show that the K-means clustering method successfully segments customers based on similar behavior patterns, which can be used for market segmentation and strategy development. The application of this algorithm in purchasing behavior analysis is expected to provide deep insights and support better business decision-making, offering a competitive advantage for companies.

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Published
2024-08-30
How to Cite
Mukhtar, H., Dwi Pramaditya, I., Saputra Weisdiyanto, W., Hardian_Putra, S., Trimuawasih, D., & Auralia Rilda, A. (2024). ALGORITMA K-MEANS UNTUK PENGELOMPOKAN PERILAKU CUSTOMER. Journal of Software Engineering and Information System (SEIS), 4(2), 96-101. https://doi.org/10.37859/seis.v4i2.7615
Section
Articles
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