Optimalisasi Penentuan Klaster pada Indeks Khusus Penanganan Stunting Menggunakan Metode Agglomerative Hierarchical

Syahrul Aziz Pamungkas, Ria Dhea Layla Nur Karisma, Evawati Alisah

Abstract


Stunting is a nutrition problem that is still the main focus in developing countries, one of which is Indonesia. One of the instruments designed to measure the performance of the implementation of the stunting reduction acceleration program at the national level is the Stunting-Specific Intervention Index (Indeks Khusus Penanganan Stunting/IKPS). This study aims to group provinces in Indonesia based on a special index for handling stunting consisting of six indicators, namely health, nutrition, housing, food, education, and social protection indicators using the agglomerative hierarchical clustering method. The agglomerative hierarchical clustering method is divided into several methods, including single linkage, complete linkage, average linkage, and ward methods. This study compares the four methods with the aim of obtaining the best cluster solution in the grouping of provinces in Indonesia based on the stunting-specific intervention index. The determination of the best method in agglomerative hierarchical clustering is determined by the value of the cophenetic correlation coefficient. The results show that the average linkage method provides a better cluster solution than other methods. The cluster solution in the average linkage method produces eight clusters, including, cluster 1 consists of one province, cluster 2 consists of nine provinces, cluster 3 consists of twelve provinces, cluster 4 consists of six provinces, cluster 5 consists of one province, cluster 6 consists of one province, cluster 7 consists of three provinces, and cluster 8 consists of one province in Indonesia.


Keywords


Stunting, Stunting-Specific Intervention Index; Agglomerative Hierarchical Clustering; Average Linkage; Cophenetic

Full Text:

PDF

References


Rahmadhita, K. (2020). Permasalahan Stunting dan Pencegahannya. Jurnal Ilmiah Kesehatan Sandi Husada, 11(1), 225–229. https://doi.org/10.35816/jiskh.v10i2.253

Candra, A. (2020). Epidemiologi Stunting. Fakultas Kedokteran Universitas Diponegoro.

BPS. (2022). Laporan Indeks Khusus Penanganan Stunting 2020-2021. Badan Pusat Statistik.

De Onis, M., Borghi, E., Arimond, M., Webb, P., Croft, T., Saha, K., De-Regil, L. M., Thuita, F., Heidkamp, R., Krasevec, J., Hayashi, C., & Flores-Ayala, R. (2019). Prevalence thresholds for wasting, overweight and stunting in children under 5 years. Public Health Nutrition, 22(1), 175–179. https://doi.org/10.1017/S1368980018002434.

BPS. (2021). Laporan Indeks Khusus Penanganan Stunting 2020-2021. Badan Pusat Statistik.

Pratiwi, S. I., Widiharih, T., & Hakim, A. R. (2019). Analisis Klaster Metode Ward Dan Average Linkage Dengan Validasi Dunn Index Dan Koefisien Korelasi Cophenetic (Studi Kasus: Kecelakaan Lalu Lintas Berdasarkan Jenis Kendaraan Tiap Kabupaten/Kota di Jawa Tengah Tahun 2018). Jurnal Gaussian, 8(4), 486–495. https://doi.org/10.14710/j.gauss.v8i4.26747

Nugraha, A., Asnawi, M. H., Rahmah, P. F., & Purwandari, T. (2021). Analisis Klaster Hirarki untuk Mengelompokan Provinsi di Indonesia berdasarkan Indikator Kesejahteraan Rakyat. E-Prosiding Seminar Nasional Statistika| Departemen Statistika FMIPA Universitas Padjadjaran, 10, 1–12.

Wahyuni, I., & Wulandari, S. P. (2022). Pemetaan Kabupaten / Kota di Jawa Timur Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Analisis Cluster Hierarki. Jurnal Sains Dan Seni, 11(1), D70–D75.

Pratiwi, S. I., Widiharih, T., & Hakim, A. R. (2019). Analisis Klaster Metode Ward Dan Average Linkage Dengan Validasi Dunn Index Dan Koefisien Korelasi Cophenetic (Studi Kasus: Kecelakaan Lalu Lintas Berdasarkan Jenis Kendaraan Tiap Kabupaten/Kota di Jawa Tengah Tahun 2018). Jurnal Gaussian, 8(4), 486–495. https://doi.org/10.14710/j.gauss.v8i4.26747

Zen, M. A., Wahyuningsih, S., & Dani, A. T. R. (2022). Aplikasi Pendekatan Agglomerative Hierarchical Time Series Clustering untuk Peramalan Data Harga Minyak Goreng di Indonesia. Seminar Nasional Official Statistics, 2022(1), 293–302. https://doi.org/10.34123/semnasoffstat.v2022i1.1394

F.Hair, J., C. Black, W., J.Babin, B., & E.Anderson, R. (2010). Multivariate Data Analysis (Seventh). Pearson Prentice Hall.

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (Sixth). Pearson Education.

Lubis, I. A., Nugroho, S., & Swita, B. (2018). Kajian Metode Pengklasteran Hirarki dengan Berbagai Pengukuran Jarak. Sigma Mu Rho (e-Jurnal Statistika), 42–53.

Amaliana, D. D., Wulandari, S. P., & Bisnis, D. S. (2024). Pengelompokan Provinsi berdasarkan Aspek Pembangunan Pendidikan di Indonesia Tahun 2023 menggunakan Analisis Cluster.

Jafarzadegan, M., Safi-Esfahani, F., & Beheshti, Z. (2019). Combining hierarchical clustering approaches using the PCA method. Expert Systems with Applications, 137, 1–10. https://doi.org/10.1016/j.eswa.2019.06.064




DOI: https://doi.org/10.18860/jrmm.v4i2.31182

Refbacks

  • There are currently no refbacks.