PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN ALGORITMA PAMDAN ALGORITMA DBSCAN BERDASARKAN INDIKATOR KELUARGA SEHAT

Inez Clarissa Nababan; Puspita Kartikasari; Ardiana Alifatus Sa’adah

doi:10.14710/j.gauss.15.2.312-319

DOI: https://doi.org/10.14710/j.gauss.15.2.312-319

PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN ALGORITMA PAMDAN ALGORITMA DBSCAN BERDASARKAN INDIKATOR KELUARGA SEHAT

*Inez Clarissa Nababan - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

Puspita Kartikasari - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

Ardiana Alifatus Sa’adah - Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

BibTex Citation Data :

@article{J.Gauss41102,
    author = {Inez Nababan and Puspita Kartikasari and Ardiana Sa’adah},
    title = {PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN ALGORITMA PAMDAN ALGORITMA DBSCAN BERDASARKAN INDIKATOR KELUARGA SEHAT},
    journal = {Jurnal  Gaussian},
  volume = {15},
    number = {2},
    year = {2026},
    keywords = {Healthy Family; Cluster Analysis; PAM; DBSCAN; Silhouette Index},
    abstract = { The healthy family index is an index used to provide an overview of the health conditions in a family. The health status of families in Central Java Province varies in each district/city, so health development priorities are also different. Based on indications of a healthy family, this study seeks to classify Central Java Province's districts and cities to determine the high/low quality of healthy families in each district/city in order to aid the government in maximizing its efforts to enhance health. The combination of district/city in Central Java Province was carried out using cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Partitioning Around Medoids (PAM) algorithm. The outcomes of grouping using the PAM algorithm obtained 4 clusters where cluster one had 16 cluster members, cluster two had 10 cluster members, cluster three had 8 cluster members, and cluster four had 1 cluster members. Grouping using the DBSCAN algorithm produces 2 clusters and 3 noise. There are many members in each cluster, namely cluster one has 29 cluster members and cluster two has 3 cluster members. Comparing the grouping results using the silhouette index value, it was found that the DBSCAN algorithm was the best algorithm with a value of the silhouette index of 0.1425. The description of the characteristics obtained is that cluster two is a cluster that has a lower average of 6 healthy family indicators compared to cluster one. },
   issn = {2339-2541},   pages = {312--319}  doi = {10.14710/j.gauss.15.2.312-319},
    url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/41102}
}

Citation Format:

Abstract

The healthy family index is an index used to provide an overview of the health conditions in a family. The health status of families in Central Java Province varies in each district/city, so health development priorities are also different. Based on indications of a healthy family, this study seeks to classify Central Java Province's districts and cities to determine the high/low quality of healthy families in each district/city in order to aid the government in maximizing its efforts to enhance health. The combination of district/city in Central Java Province was carried out using cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Partitioning Around Medoids (PAM) algorithm. The outcomes of grouping using the PAM algorithm obtained 4 clusters where cluster one had 16 cluster members, cluster two had 10 cluster members, cluster three had 8 cluster members, and cluster four had 1 cluster members. Grouping using the DBSCAN algorithm produces 2 clusters and 3 noise. There are many members in each cluster, namely cluster one has 29 cluster members and cluster two has 3 cluster members. Comparing the grouping results using the silhouette index value, it was found that the DBSCAN algorithm was the best algorithm with a value of the silhouette index of 0.1425. The description of the characteristics obtained is that cluster two is a cluster that has a lower average of 6 healthy family indicators compared to cluster one.

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Keywords: Healthy Family; Cluster Analysis; PAM; DBSCAN; Silhouette Index

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Section: Articles

Language : EN

In Vol 15, No 2 (2026): Jurnal Gaussian

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