Komparasi Distance Measure pada K-Means dalam Klasterisasi Peserta KB Aktif

Abstract

The rapid population growth in Indonesia poses significant challenges to public welfare, economic stability, and sustainable development. The Family Planning program aims to regulate population growth through various contraceptive methods; however, participation rates often differ across regions. Understanding these variations is crucial for designing targeted interventions. This study investigates how different distance measures in the K-Means clustering algorithm affect the segmentation quality of KB participants in Kalirejo Village, Lawang District. Eight distance metrics—Euclidean, Manhattan, Minkowski, Chebyshev, Mahalanobis, Bray-Curtis, Canberra, and Cosine—were compared using standardized data from the local BKKBN office (January–September). Cluster validity was evaluated using the Silhouette Coefficient across k=2–10. Results show that the Manhattan distance with k=2 achieved the best clustering quality (SC = 0.7191), effectively distinguishing participant groups by contraceptive method preference. The study highlights the importance of selecting suitable distance measures to improve data-driven policy and decision-making in family planning management