Medoid-based Clustering pada Kecamatan di Kabupaten Lebak dan Pandeglang Provinsi Banten Berdasarkan Trilogi Ketahanan Pangan

Author:

Budiaji Weksi,Pancawati Juwarin

Abstract

Lebak and Pandeglang Regions in Banten Province have a high stunting prevalence of children under 5 years old and have the lowest value of food security index among regions in Banten Province. Cluster analysis to group districts in Lebak and Padeglang Regions is indispensable to characterize the district members in those two regions. The variables applied to calculate distance between districts in  a simple k-medoid clustering were trilogy of food security namely the availability, access, and utility of the food from Bureau of Statistics of Lebak and Pandeglang Regions 2019 data. The distances were varied among Euclidean, squared Euclidean, and Manhattan distances. The clustering result was then validated via consensus clustering and internal validation. The suitable number of clusters was four defined as the available and access cluster (cluster 1), the access cluster (cluster 2), the vulnerable cluster (cluster 3), and the available cluster (cluster 4). The cluster 3 as the vulnerable cluster should be focused on because it consists of 38% from overall districts in Lebak and Banten Regions.

Publisher

Universitas PGRI Adi Buana Surabaya

Subject

General Medicine

Reference15 articles.

1. Balitbangkes. 2019. Laporan Provinsi Banten Riskesdas 2018/ Badan Penelitian dan Pengembangan Kesehatan. Jakarta : Lembaga Penerbit Badan Penelitian dan Pengembangan Kesehatan.

2. BPS Lebak. 2019. Kabupaten Lebak dalam Angka. Lebak: Badan Pusat Statistik Kabupaten Lebak.

3. BPS Pandeglang. 2019. Kabupaten Pandeglang dalam Angka. Pandeglang: Badan Pusat Statistik Kabupaten Lebak.

4. Budiaji W. 2019. Medoid-based shadow value validation and visualization. International Journal of Advances in Intelligent Informatics Vol. 5 (2), pp 76-88

5. Budiaji, Weksi, and Friedrich Leisch. 2019. Simple K-Medoids Partitioning Algorithm for Mixed Variable Data. Algorithms 12, no. 9: 177. https://doi.org/10.3390/a12090177

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