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All Journal KOMPUTIKA - Jurnal Sistem Komputer
Crisintha, Debora
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Segmentasi Pola Pembatalan Pemesanan Layanan di Salon Nail Art XYZ Menggunakan K-Means Clustering dan Evaluasi dengan Davies-Bouldin Index Banusu, Junita Gregoria; Banusu, Junita; Crisintha, Debora; Baso, Budiman; Herlina Ullu, Hevi
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.15937

Abstract

The beauty industry, particularly nail art, is experiencing rapid growth as consumer demand for nail care services increases. However, the high booking cancellation rate is a challenge that can reduce operational efficiency and customer loyalty. This research focuses on segmenting XYZ Nail Art Salon customers based on cancellation patterns by applying the K-Means algorithm and evaluating using the Davies-Bouldin Index (DBI). In contrast to previous research which is generally conducted in e-commerce, hotels, and retail, this research focuses on the beauty service industry which is still rarely explored. The data used comes from the Kaggle platform with 400 order data. The analysis process includes pre-processing, key feature selection (Cancel Description and Days), and K-Means implementation. The clustering results showed two main segments: 288 customers with planned cancellation patterns and 112 customers with spontaneous cancellation patterns. Evaluation using DBI yielded a value of 0.399 indicating good clustering quality. This segment distinction has practical implications, such as automatic reminder strategies for customers with planned cancellations, as well as providing schedule flexibility or special promotions for customers with spontaneous cancellations. This research contributes to providing data-driven insights for salon management to devise more targeted marketing strategies, reducing the level of customer churn.
Co-Authors Banusu, Junita Banusu, Junita Gregoria Baso, Budiman Herlina Ullu, Hevi