Algoritma K-Means untuk Clustering Provinsi di Indonesia Berdasarkan Kasus Stunting

Authors

  • Syelvia Julianti Universitas Udayana Author
  • I Made Widiartha Universitas Udayana Author

DOI:

https://doi.org/10.24843/JNATIA.2024.v02.i03.p16

Keywords:

Stunting, K-Means, Elbow Method, Silhouette Coefficient, Devies Bouldin Index

Abstract

Stunting is a nutritional issue that poses a global challenge, especially in developing countries like Indonesia. According to UNICEF, Indonesia ranks among the top five countries with the highest stunting prevalence. To address this issue, clustering provinces in Indonesia each year can help ensure equitable food distribution and other resources. This can be done using the KMeans clustering algorithm, with the optimal number of clusters determined by the elbow method and evaluated using the silhouette coefficient and Davies-Bouldin index. The optimal number of clusters was found to be 3, with a silhouette coefficient of 0.50 and a Davies-Bouldin index of 0.70. In 2020, there were 15 provinces in cluster 1, 6 provinces in cluster 2, and 17 provinces in cluster 3. In 2021, 15 provinces were in cluster 1, 17 in cluster 2, and 6 in cluster 3. In 2022, there were 17 provinces in cluster 1, 14 in cluster 2, and 7 in cluster 3. In 2023, 5 provinces were in cluster 1, 14 in cluster 2, and 19 in cluster 3. By 2024, there were 18 provinces in cluster 1, 17 in cluster 2, and 3 in cluster 3. 

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Published

2024-05-01

How to Cite

[1]
Syelvia Julianti and I Made Widiartha, “Algoritma K-Means untuk Clustering Provinsi di Indonesia Berdasarkan Kasus Stunting”, Jnatia, vol. 2, no. 3, pp. 573–582, May 2024, doi: 10.24843/JNATIA.2024.v02.i03.p16.

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