Lea Angelina, Dinda Meyda Putri, Nisfatun Nurul Ana, Elsa Izza Syafira, Kamilah Citra Chumairoh, Nabbila Dyah Syaharani, Fatkhurokhman Fauzi

Clustering of Maternal and Child Health Indicators in Indonesia Using Hierarchical Clustering Agglomerative

Introduction

Clustering of maternal and child health indicators in indonesia using hierarchical clustering agglomerative. Clustering Indonesian provinces by maternal & child health indicators using hierarchical clustering reveals six distinct groups. Findings highlight disparities, especially in Papua, urging tailored interventions.

Abstract

Maternal and child health is a top priority in national development, given that high maternal and infant mortality rates remain a significant challenge in Indonesia. Disparities in health indicators between regions indicate that existing inequalities remain insufficiently addressed. This study aims to group 38 provinces in Indonesia based on 14 maternal and child health indicators for 2024 to identify patterns of disparity. The method used is Agglomerative Hierarchical Clustering with multicollinearity tests (VIF) and KMO for data validation. The complete linkage method was selected for its optimal performance, yielding an agglomerative coefficient of 0.742 and the highest silhouette value of 0.2099 at K = 6. The results formed six clusters reflecting similarities in regional characteristics. Several provinces in Papua clustered separately due to their low health indicator achievements. These findings emphasize the need for region-specific intervention policies to address disparities and promote equitable improvements in maternal and child health.

Full Text

Comments

You need to be logged in to post a comment.