SEGMENTASI JAMAAH UMRAH MENGGUNAKAN K-MEANS DENGAN METODE ELBOW GUNA STRATEGI PENINGKATANMANAJEMEN SUMBER DAYA MANUSIA
DOI:
https://doi.org/10.54314/jssr.v9i3.6757Keywords:
jamaah umrah, K-Means Clustering, Manajemen Sumber Daya Manusia, Metode Elbow, performance distanceAbstract
Abstract: The increasing number of Umrah pilgrims requires travel agencies to implement data-driven approaches to improve service quality and support Human Resource Management (HRM). This study aims to segment Umrah pilgrims using the K-Means clustering algorithm as a basis for HRM strategies. The research follows the Knowledge Discovery in Databases (KDD) process, including data preprocessing, clustering in RapidMiner, and cluster evaluation using the Elbow Method based on performance distance (average within centroid distance). Euclidean Distance was used to measure similarity among data objects. Seven clustering experiments were conducted with k = 2–8, producing performance distance values of 3418.971, 1416.677, 865.316, 479.795, 344.203, 258.804, and 214.562, respectively. The Elbow curve indicates that k = 3 is the optimal number of clusters because it represents the most significant decrease before the curve stabilizes. The resulting clusters provide objective information for supporting staff placement, service task allocation, pilgrim assistance planning, and employee competency development. Therefore, the integration of K-Means and the Elbow Method offers an effective data-driven approach for supporting HRM decision-making in Umrah travel agencies.
Keyword: Elbow Method; Human Resource Management; K-Means Clustering; performance distance; Umrah pilgrims.
Abstrak: Peningkatan jumlah jamaah umrah mendorong biro perjalanan memanfaatkan analisis data untuk meningkatkan kualitas pelayanan dan mendukung pengambilan keputusan pada Manajemen Sumber Daya Manusia (MSDM). Penelitian ini bertujuan melakukan segmentasi jamaah umrah menggunakan algoritma K-Means Clustering sebagai dasar penyusunan strategi MSDM. Metode penelitian mengikuti tahapan Knowledge Discovery in Databases (KDD), meliputi data preprocessing, proses klasterisasi pada RapidMiner, serta evaluasi menggunakan Metode Elbow berdasarkan performance distance (average within centroid distance). Pengukuran kemiripan data dilakukan menggunakan Euclidean Distance. Pengujian dilakukan sebanyak tujuh kali dengan variasi k = 2–8, menghasilkan nilai performance distance berturut-turut 3418,971; 1416,677; 865,316; 479,795; 344,203; 258,804; dan 214,562. Hasil evaluasi menunjukkan bahwa k = 3 merupakan jumlah klaster optimal karena membentuk titik siku (elbow point) dengan penurunan nilai paling signifikan sebelum kurva melandai. Hasil segmentasi dapat dimanfaatkan sebagai dasar penempatan pegawai, pembagian tugas pelayanan, penyusunan tim pendamping jamaah, dan pengembangan kompetensi pegawai secara lebih tepat sasaran.
Kata kunci: jamaah umrah; K-Means Clustering; Manajemen Sumber Daya Manusia; Metode Elbow; performance distance.
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