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All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Amrul Hadiyanoor
STMIK Indonesia Banjarmasin
Computer Science & IT Electrical & Electronics Engineering Engineering

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Real-Time K-Means Clustering with Firebase ML Kit: Segmenting Barber Shop Customers by Booking Behavior and Churn Risk Rafie Rafie; Helda Yunita; Amrul Hadiyanoor
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i1.461

Abstract

This study addresses the challenges of high churn rates and the need for real-time customer behavior analysis in barber shops by developing a K-Means model integrated with Firebase ML Kit. The research analyzes 70,000 booking records from an Android application, focusing on features such as booking frequency, average cancellations, and recency day. The model achieves optimal performance with 5 clusters, validated by a Silhouette Score of 0.58 and a Davies-Bouldin Index of 0.541. Key segments like "Inactive Members" and "High Volume Churners" are successfully identified, enabling targeted business strategies such as reactivation campaigns and priority booking offers. The system is implemented in a mobile application, providing real-time customer segmentation and actionable insights. This approach offers a scalable solution to enhance customer retention and operational efficiency in the barber shop industry
Co-Authors Helda Yunita Rafie Rafie