Perkembangan teknologi informasi menuntut sistem reservasi yang lebih efektif dan efisien. Pada Marshal Futsal, proses pemesanan masih dilakukan secara manual, informasi jadwal belum real-time, dan pemanfaatan data pelanggan belum optimal. Penelitian ini bertujuan untuk merancang dan membangun sistem Customer Relationship Management operasional berbasis web yang terintegrasi dengan analisis segmentasi pelanggan menggunakan metode Recency, Frequency, Monetary (RFM) dan algoritma K-Means Clustering. Metode penelitian yang digunakan adalah deskriptif kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan studi literatur. Analisis data dilakukan melalui tahapan perhitungan nilai RFM, normalisasi, serta proses clustering menggunakan algoritma K-Means untuk mengelompokkan pelanggan berdasarkan perilaku transaksi. Hasil penelitian menunjukkan bahwa sistem mampu menyediakan informasi jadwal secara real-time dan meminimalkan kesalahan pencatatan seperti double booking. Selanjutnya, penerapan RFM dan K-Means berhasil menghasilkan segmentasi pelanggan yang dapat mendukung pengambilan keputusan dalam meningkatkan pelayanan dan strategi pemasaran.

The advances of information technology demand more effective and efficient reservation systems. At Marshal Futsal, the booking process is still done manually, schedule information is not real-time, and the utilization of customer data has not been optimal yet. This study aims to design and develop a web-based operational Customer Relationship Management (CRM) system integrated with customer segmentation analysis using the Recency, Frequency, Monetary (RFM) method and the K-Means Clustering algorithm. The research method used is qualitative descriptive, with data collection techniques including observation, interviews, and literature review. Data analysis involves calculating RFM values, normalization, and clustering using the K-Means algorithm to group customers based on transaction behavior. The results of the study indicate that the system is capable of providing real-time schedule information and minimizing recording errors such as double bookings. Furthermore, the application of RFM and K-Means successfully produces customer segmentation that can support decision making in improving service and marketing strategies.