Clustering Analysis of Stunting Risk Factors Using K-Means and Principal Component Analysis: A Case Study in Indonesian Regency

Abstract

Stunting, characterized by impaired growth and development in children, is one of the most serious public health problems often caused by chronic malnutrition. This study aims to identify patterns among stunting cases through clustering analysis of child health data. The algorithm used in this research uses K-Means. The dataset used in this study uses health data from 599 children in the Sambas Regency area of East Kalimantan Province. This dataset has several features that are quite diverse such as height, weight, age, nutritional intake, socioeconomic status, and others. This research process begins with cleaning the data, as well as looking at the correlation between features. One of the methods used is to conduct a data analysis process using Principal Component Analysis (PCA) which aims to reduce the dimensions of the data. After that, the process of finding the number of clusters using the Elbow method is carried out to determine the optimal number of clusters. This research uses 4 clusters in the process. The clustering results revealed that family structure (main family vs extended family) and parental income levels significantly influence stunting prevalence in the region.