Penerapan Algoritma K-Means Berbasis Model RFM untuk Klasterisasi Anggota di Perpustakaan Daerah Kuala Kapuas
DOI:
https://doi.org/10.69688/juksit.v5i1.187Keywords:
Data mining, K-Means, Perpustakaan Daerah, RFM, Segmentasi AnggotaAbstract
Perpustakaan daerah belum memiliki mekanisme berbasis data untuk membedakan anggota berdasarkan pola kunjungan dan peminjaman, sehingga layanan masih diberikan secara seragam kepada seluruh anggota. Penelitian ini bertujuan menerapkan model Recency, Frequency, Monetary (RFM) dikombinasikan dengan Algoritma K-Means untuk menghasilkan segmentasi berbasis data terhadap 1.430 anggota Dinas Kearsipan dan Perpustakaan Daerah Kuala Kapuas. Metode yang digunakan meliputi konstruksi variabel RFM dari data kunjungan dan peminjaman, normalisasi MinMaxScaler, penentuan jumlah klaster optimal menggunakan Elbow Method, implementasi K-Means, serta validasi kualitas klaster menggunakan Silhouette Score, Davies-Bouldin Index, dan Calinski-Harabasz Index. Hasil penelitian menunjukkan K=3 sebagai jumlah klaster optimal dengan nilai Silhouette Score sebesar 0,3478. Tiga klaster yang terbentuk adalah Anggota Aktif (199 anggota, 13,9%), Anggota Sedang (570 anggota, 39,9%), dan Anggota Pasif (661 anggota, 46,2%), dengan dimensi Frequency sebagai pembeda utama antar klaster. Anggota Pasif tercatat memiliki tingkat kepuasan layanan yang tinggi meskipun jarang berkunjung, yang mengindikasikan hambatan akses sebagai penyebab kepasifan. Berdasarkan profil tiap klaster, dirumuskan tiga strategi layanan terdiferensiasi berupa retensi untuk Anggota Aktif, peningkatan keterlibatan untuk Anggota Sedang, dan reaktivasi untuk Anggota Pasif.
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