Kombinasi Principal Component Analysis dengan Algoritma K-Means untuk Klasterisasi Data Stunting
DOI:
https://doi.org/10.30865/klik.v5i1.1977Keywords:
Stunting; K-Means; Principal Component Analysis; Elbow Method; Silhouette MethodAbstract
Dompu Regency in West Nusa Tenggara ranks as the fourth highest regency in terms of stunting prevalence among toddlers in the NTB province in 2022, with a rate of 34.5%. This study utilizes data from the Dompu Regency Health Office in 2023, covering 81 villages and six variables that influence the prevalence of stunting. The objective of this research is to determine and understand the characteristics of areas based on stunting factors in Dompu Regency using the K-Means Clustering method combined with Principal Component Analysis (PCA). The K-Means method produces clusters that represent areas with different characteristics, derived from the data reduction results of PCA, which form the principal components. The optimization of the number of clusters using the Elbow method indicates 3 clusters, consisting of Zone Type 1 with 42 villages, Zone Type 2 with 12 villages, and Zone Type 3 with 27 villages. Subsequently, an evaluation phase using the Silhouette method resulted in 2 clusters: Zone Type 1 with 54 villages and Zone Type 2 with 22 villages, with a Silhouette Score of 0.53, indicating a fairly good cluster structure. PCA produced two principal components with the highest eigenvalues, each explaining 58.7% and 14.28% of the variance, with a cumulative variance of 72.9%. This demonstrates that these two principal components can effectively represent the factors influencing stunting prevalence in Dompu Regency.
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