Comparison of K-Means and Fuzzy C-Means for Regional Clustering Based on Unmet Need Determinants in East Java
DOI:
https://doi.org/10.30871/jaic.v10i2.12411Keywords:
Clustering, Fuzzy C-Means, K-Means, Unmet needAbstract
Unmet need for family planning (KB) refers to a condition in which fertile couples wish to delay or stop pregnancy but are not using any contraceptive methods. In 2022, East Java recorded a relatively high level of unmet need, indicating the need for a data-driven strategy to improve the effectiveness of the family planning program. This study compares the performance of the K-Means and Fuzzy C-Means (FCM) algorithms in clustering regencies/cities based on key determinants of unmet need, namely the number of fertile-age couples, poverty index, median age at first marriage, and female labor force participation rate. Secondary data obtained from BKKBN in 2023 were processed using data normalization, clustering, and evaluation with the Silhouette Coefficient and Davies–Bouldin Index, followed by denormalization to interpret the clustering results. The results indicate that K-Means outperforms Fuzzy C-Means in clustering regions based on the analyzed characteristics. After four iterations, the clustering results classify 22 regencies/cities into a low unmet-need cluster, 3 regencies into a moderate unmet-need cluster, and 13 regencies/cities into a high unmet-need cluster, achieving a Silhouette Coefficient value of 0.6997 and a Davies–Bouldin Index of 0.3539.
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