This Author published in this journals
All Journal Jurnal Gaussian
Inez Clarissa Nababan
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

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
Jurnal Gaussian Vol 15, No 2 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.2.312-319

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.