IDENTIFYING THE CLUSTER OF FAMILIES AT RISK OF STUNTING IN YOGYAKARTA USING HIERARCHICAL AND NON-HIERARCHICAL APPROACH

Authors

  • Ersa Riga Puspita Universitas Islam Indonesia, Indonesia
  • Mujiati Dwi Kartikasari Universitas Islam Indonesia, Indonesia

DOI:

https://doi.org/10.21107/kursor.v12i4.358

Keywords:

Cluster, Fuzzy C-Means, Hierarchical Clustering, Non-Hierarchical Clustering, Stunting, Ward

Abstract

Stunting, or short stature, is a growth disorder usually caused by chronic dietary deficiencies from the prenatal stage to early childhood, typically becoming evident in children after the age of 2. Stunting cases in Yogyakarta Province experienced a decline in 2020. With this development, the government aims to achieve zero stunting in Yogyakarta Province by 2024. To support this goal, a research study was conducted in 2021 to analyze family factors associated with stunting risks in Yogyakarta Province. The study aimed to assist the government in addressing the issue and achieving the target. In this research, a hierarchical clustering algorithm using the Ward technique and a non-hierarchical clustering algorithm using the Fuzzy C-Means (FCM) approach were applied. The optimal number of clusters was determined using the average distance and figure of merit approach. Stability validation, which also used the average distance and figure of merit approach, demonstrated that the best results were achieved by the non-hierarchical clustering algorithm employing FCM. As a result, six clusters were identified: cluster 1 with 5 sub-districts, cluster 2 with 18 sub-districts, cluster 3 with 21 sub-districts, cluster 4 with 17 sub-districts, cluster 5 with 14 sub-districts, and cluster 6 with 3 sub-districts.

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References

[1] Kemenkes, “Pencegahan Stunting Pada Anak,” Direktorat Promosi Kesehatan dan Pemberdayaan Masyarakat, 2019. https://promkes.kemkes.go.id/pencegahan-stunting (accessed Jul. 02, 2023).

[2] D. Satriawan and D. A. Styawan, “Pengelompokkan Provinsi di Indonesia Berdasarkan Faktor Penyebab Balita Stunting Menggunakan Analisis Cluster Hierarki,” J. Stat. dan Apl., vol. 5, no. 1, pp. 61–70, 2021, doi: https://doi.org/10.21009/JSA.05106.

[3] K. Komalasari, E. Supriati, R. Sanjaya, and H. Ifayanti, “Faktor-Faktor Penyebab Kejadian Stunting Pada Balita,” Maj. Kesehat. Indones., vol. 1, no. 2, pp. 51–56, 2020, doi: 10.47679/makein.202010.

[4] Pemerintah Kota Yogyakarta, “Tahun 2024 Targetkan Kota Yogya Zero Stunting,” Portal Berita Pemerintah Kota Yogyakarta, 2024. https://warta.jogjakota.go.id/detail/index/23501 (accessed Jul. 02, 2023).

[5] D. Siahaan and E. Prasetyo, “Perbaikan Metode Pemeringkatan Spesifikasi kebutuhan Berdasarkan Perkiraan Keuntungan dan Nilai Proyek dengan Mengurangi Perbandingan Berpasangan,” J. Ilm. Kursor, vol. 6, no. 2, pp. 93–102, 2011.

[6] Imasdiani, I. Purnamasari, and F. D. T. Amijaya, “Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward,” Eksponensial, vol. 13, no. 1, pp. 9–18, 2022.

[7] D. L. Pardosi and I. D. Siagian, “Klasterisasi Data Lowongan Pekerjaan Berdasarkan Fuzzy C-Means,” J. Ilmu Komput. dan Sist. Inf., vol. 3, no. 1, pp. 27–31, 2020, doi: https://doi.org/10.9767/jikomsi.v3i1.79.

[8] I. Suciati, N. Herawati, S. Subian, and Widiarti, “Analisis Klaster Menggunakan Metode Fuzzy C-Means pada Data COVID-19 di Provinsi Lampung,” 2022.

[9] L. Affandi, R. Arianto, and H. H. Firdausy, “Implementasi Algoritma Fuzzy C-Means pada Kasus Stunting Balita Berbasis Website,” 2021.

[10] A. E. Haryati, Sugiyarto, and R. D. A. Putri, “Comparison of Fuzzy Subtractive Clustering and Fuzzy C-Means,” J. Ilm. Kursor, vol. 11, no. 1, 2021, doi: https://doi.org/10.21107/kursor.v11i1.254.

[11] J. Inayah, D. A. S. N. Maghfiroh, and D. C. R. Novitasari, “Clustering Daerah Rawan Kriminalitas Menggunakan Algoritma Fuzzy C-Means,” J. Ilm. Inform. Komput., vol. 27, no. 2, pp. 95–106, 2022, doi: https://doi.org/10.35760/ik.2022.v27i2.6019.

[12] W. Sanusi, A. Zaky, and B. N. Afni, “Analisis Fuzzy C-Means dan Penerapannya Dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Faktor-faktor Penyebab Gizi Buruk,” J. Math. Comput. Stat., vol. 2, no. 1, 2019, doi: https://doi.org/10.35580/jmathcos.v2i1.12458.

[13] M. Paramadina, S. Sudarmin, and M. K. Aidid, “Perbandingan Analisis Cluster Metode Average Linkage dan Metode Ward (Kasus: IPM Provinsi Sulawesi Selatan),” Variansi J. Stat. Its Appl. Teach. Res., vol. 1, no. 2, 2019, doi: https://doi.org/10.35580/variansiunm9357.

[14] Y. I. Hartanto, A. Rusgiyono, and T. Wuryandari, “Penerapan Analisis Klaster Metode Ward Terhadap Kabupaten/Kota di Jawa Tengah Berdasarkan Pengguna Alat Kontrasepsi,” J. Gaussian, vol. 6, no. 4, 2017, doi: https://doi.org/10.14710/j.gauss.6.4.528-537.

[15] G. Brock, V. Pihur, S. Datta, and S. Datta, “clValid : An R Package for Cluster Validation,” J. Stat. Softw., vol. 25, no. 4, 2008, doi: 10.18637/jss.v025.i04.

[16] I. M. S. Bimantara and I. M. Widiartha, “Optimization of K-Means Clustering Using Particle Swarm Optimization Algorithm For Grouping Traveler Reviews Data on Tripadvisor Sites,” J. Ilm. Kursor, vol. 12, no. 1, 2023, doi: https://doi.org/10.21107/kursor.v12i01.269.

[17] Mahmudi, R. Goejantoro, and F. D. T. Amijaya, “Perbandingan Metode C-Means dan Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator IPM Tahun 2019,” Eksponensial, vol. 12, no. 2, 2021.

[18] Y. Muflihan, H. Retnawati, and A. Kristian, “Analisis Cluster dengan Metode Hierarki untuk Pengelompokan Sekolah Menengah Atas Berdasarkan Rapor Mutu Sekolah di Kabupaten Nagan Raya,” Meas. Educ. Res., vol. 2, no. 1, pp. 22–33, 2022.

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Published

2024-12-06

Issue

Vol. 12 No. 4 (2024)

Section

Articles

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