Pemanfaatan Data Mining Dalam Penentuan Penyuluhan Penyakit Stunting Menggunakan Partitioning Around Medoids (PAM)

Authors

  • Allsela Meiriza Universitas Sriwijaya
  • Endang Lestari Fakultas Ilmu Komputer, Universitas Sriwijaya
  • Pacu Putra Fakultas Ilmu Komputer, Universitas Sriwijaya
  • Nabila Rizky Oktadini Fakultas Ilmu Komputer, Universitas Sriwijaya
  • Meitiana Audya Fakultas Ilmu Komputer, Universitas Sriwijaya

https://doi.org/10.36342/teika.v12i02.2935

Keywords:

Data Mining, PAM, Stunting

Abstract

Stunting is a state of malnutrition that is dangerous during the growth period from the beginning of human life. For this reason, early prevention is needed, currently ,the Government, especially in the health sector such as the Health Office, has assisted in socializing or counseling stunting. However, the problem odetermining the distribution there are difficulties in determining it, especially in the Family Health and Community Nutrition Section of the Palembang City Health Office, due to the large number of Puskesmas in each Sub-district in Palembang City, so to help this, the purpose of this study is to apply the Partitioning Around Medoids method in helping to determine stunting disease counseling appropriately. The method used is PAM. The results of this study are in the form of clusters that are divided into two, namely groups 1 and 2. Cluster 0 obtained 9 Puskesmas, while cluster 1 amounted to 23 Puskesmas, the priority in determining couseling was in cluster 0. Furthermore, the number 0.272 was obtained, meaning that the cluster evaluation was quite good because the value was close to 0 from the test using the Davies Bouldin Index.

 

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Published

2022-10-31

How to Cite

Meiriza, A., Lestari, E., Putra, P., Oktadini, N. R., & Audya, M. (2022). Pemanfaatan Data Mining Dalam Penentuan Penyuluhan Penyakit Stunting Menggunakan Partitioning Around Medoids (PAM). TeIKa, 12(02), 89-96. https://doi.org/10.36342/teika.v12i02.2935

Issue

Vol. 12 No. 02 (2022): TeIKa: October 2022

Section

Datamining

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