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All Journal Publika Jurnal Life Science Journal of Software Engineering and Information System (SEIS) Jurnal Informatika Polinema (JIP) Jurnal Dinamika Sosial dan Sains
RIZKY ARDIANSYAH
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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PEMODELAN RFM & K-MEANS CLUSTERING UNTUK SEGMENTASI PELANGGAN DALAM PENJUALAN ONLINE Lukas, Ivander; Finanta Okmayura; Aidha Tita Irani; Ernia Juliastuti; Muhammad Amirulhaq; Rizky Ardiansyah; Sherly Fillia
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9556

Abstract

The exponential growth of e-commerce platforms necessitates sophisticated customer analytics to maintain competitive advantage and optimize revenue streams. This study addresses the critical challenge of understanding heterogeneous customer purchasing behaviors in online retail environments through advanced data mining techniques. The research implements RFM (Recency, Frequency, Monetary) modeling integrated with K-Means clustering algorithm to achieve comprehensive customer segmentation for strategic marketing optimization. A quantitative-exploratory methodology was employed, utilizing a comprehensive online sales dataset comprising over 40,000 transactional records. The analytical framework involved systematic data preprocessing using Python libraries (Pandas, NumPy), followed by RFM parameter calculation and standardization through StandardScaler normalization. K-Means clustering was subsequently applied with optimal cluster determination via Elbow Method validation, yielding three distinct customer segments. Visualization and interpretation were conducted using Tableau, Matplotlib, and Seaborn for comprehensive segment characterization. Results demonstrate successful identification of strategically significant customer clusters: high-value loyal customers, moderate-engagement prospects, and potential churn-risk segments, each exhibiting distinctive RFM behavioral patterns. The segmentation framework enables targeted marketing strategy formulation, personalized customer retention programs, and optimized resource allocation. This research contributes valuable insights for e-commerce practitioners seeking data-driven approaches to enhance customer relationship management and sustain long-term business profitability in competitive online marketplaces.
Co-Authors Adi Pranoto Aidha Tita Irani Ariadi Retno Tri Hayati Ririd Atikah Lathofani Bias Paris Budi Harijanto, Budi Chevi Ardiana Rusmawan Deddy Kusbianto Dissa Ezra Nurpratika Dzaki Alwan Hamadi Erfan Rohadi Ernia Juliastuti Finanta Okmayura Lida amalia Lukas, Ivander Muhammad Amirulhaq Muhammad Aspandi Aspudji Nurannisa Khumaeroh Azzahro R. Farah Dini Qoyyimah Rachmad Jibril Al Kautsar Resa Uni Natulisiya Salsabilah Fejrinaningtias Agisti Sherly Fillia Vivi Amalia Vidiyaningsih Yuri Arianto Yuri Ariyanto