CLUSTERING OF INDONESIAN PROVINCES BASED ON LIFE EXPECTANCY USING K-MEANS WITH ELBOW METHOD EVALUATION
DOI:
https://doi.org/10.30656/jsii.v12i1.10116Abstract
Life Expectancy (LE) is an important indicator for measuring the welfare of the community and the level of health in a region. Indonesia's LE varies significantly across provinces, indicating differences in infrastructure and access to health services. The objective of this study is to use the K-Means algorithm to cluster Indonesian provinces based on LE and to evaluate the clustering results using the Elbow method to determine the ideal number of clusters. The data used includes LE from all Indonesian provinces over the past several years. To facilitate the identification of areas requiring special attention in health policy planning, provinces are classified into clusters based on comparable LE characteristics. This study focuses on optimizing the K-Means Algorithm with the Elbow method. Iterative experiments were conducted ten times, and the ideal K value was found to be K=3. The clustering results show that Cluster 0 has 103 members, Cluster 1 has 181 members, and Cluster 2 has 260 members. The research findings indicate that the K-Means algorithm successfully grouped provinces based on LE with a low variation rate within each cluster. The Elbow method helped determine the ideal number of clusters.
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