Pengelompokan Puskesmas Berdasarkan Kasus Balita Stunting di Kabupaten Paser Menggunakan Metode K-Medoids

  • Ika Puspita Universitas Mulawarman
  • Memi Nor Hayati
  • Darnah Andi Nohe

Abstract

The number of cases of stunting toddler in Paser Regency increased by 6.66% from 2018 to 2019%. The increased in the number of stunting toddler in Paser Regency shows that the efforts made by the Paser Regency Government have not been effective in reducing the prevalence of stunting toddler because the stunting toddler rate in Paser Regency is still above the threshold set by the World Health Organization (WHO), which is a maximum of 20%. Therefore, an appropriate strategy is needed to find out which areas receive special attention and treatment, one of method to be used is cluster analysis. Cluster analysis is divided into two methods, namely the hierarchical method and the non-hierarchical method. The non-hierarchical method begins by establishing the number of groups. One of the methods included in the non-hierarchical method is K-medoids. In this study, clustering will be carried out in cases of stunting toddlers in Paser Regency using the K-medoids method. This study aims to determine the optimal cluster formed by selecting the smallest Davies Buoldin Index (DBI) value from the 2019 Community Health Center grouping in Paser Regency. The clusters formed for the K-medoids method in this study were 2 clusters, 3 clusters, and 4 clusters. Based on the results of the analysis, the K-medoids method for 2 clusters, 3 clusters and 4 clusters was based on the DBI values ​​of 0.977, 1.470, and 1.670, respectively. The optimal group for classifying stunting toddler cases in Paser Regency in 2019 is 2 cluster using K-medoids method.

Downloads

Download data is not yet available.

References

Andriana, M., & Wirjatmadi, B. (2014). Gizi dan Kesehatan Balita Peranan Micro Zinc pada Pertumbuhan Balita. Jakarta: Kencana Prenadamedia Group.
Anderberg, M. R. (1973). Cluster Analysis for Applications. Academic Press, New York.
Bates, A., & Kalita, J. (2016). Counting Clusters in Twitter Posts. Proceedings of the 2 International Conference on Information Technology for Competitive Strategies, pp. 85
Dinas Kesehatan Kabupaten Paser. (2019). Capaian Program Gizi. Kabupaten Paser: Dinas Kesehatan.
Metisen, B. M., & Sari, H. L. (2015). Analisis Clustering Menggunakan Metode K-means dalam Pengelompokkan Penjualan Produk pada Swalayan Fadhila. Jurnal Media Infortama. Vol.11 No. 2.
Prasetyo, E. (2012). Data Mining: Konsep dan Aplikasi menggunakan Matlab. Yogyakarta: Andi Offset.
Supranto, J. (2004). Analisis Multivariat Arti dan Interpretasi. Jakarta: PT Rineka Cipta.
Suyanto. (2017). Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika.
Triyanto, W. A. (2015). Algoritma K-Medoids untuk Penentuan Strategi Pemasaran Produk. Jurnal SIMETRIS. Vol. 6 No. 1 Hal. 183 – 188.
Wani, M. A. & Riyaz, R. (2017). A novel point density based validity index for clustering gene expression datasets. International Journal of Data Mining and Bioinformatics 17 (1): 66–84.
Widarjono, A. (2015). Analisis Multivariat Terapan dengan Program SPSS, AMOS, dan SMARTPLS Edisi Kedua. Yogyakarta: UPM STIM YKPN.
Wirasmoyo, B. (2019). ANALISIS FAKTOR PENYEBAB KASUS STUNTING (Studi Kasus di Desa Tamanharjo Kecamatan Singosari Kabupaten Malang Provinsi Jawa Timur). Undergraduate (S1) thesis, University of Muhammadiyah Malang.
Published
2023-05-22
How to Cite
PUSPITA, Ika; HAYATI, Memi Nor; NOHE, Darnah Andi. Pengelompokan Puskesmas Berdasarkan Kasus Balita Stunting di Kabupaten Paser Menggunakan Metode K-Medoids. EKSPONENSIAL, [S.l.], v. 14, n. 1, p. 1-10, may 2023. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/1089>. Date accessed: 20 apr. 2024. doi: https://doi.org/10.30872/eksponensial.v14i1.1089.
Section
Articles