Penerapan Metode K-Means dalam Klasterisasi Status Desa terhadap Keluarga Beresiko Stunting

Dayla May Cytry; Sarjon Defit; Gunadi Nurcahyo

doi:10.35134/komtekinfo.v10i3.423

Authors

Dayla May Cytry Universitas Putra Indonesia YPTK Padang
Sarjon Defit Universitas Putra Indonesia YPTK Padang
Gunadi Nurcahyo Universitas Putra Indonesia YPTK Padang

DOI:

https://doi.org/10.35134/komtekinfo.v10i3.423

Keywords:

Stunting, Stunting in families at a risk factor, K-Means Clustering, Clusterization

Abstract

The Indonesian government issued Presidential Regulation of the Republic of Indonesia Number 72 of 2021 concerning the acceleration of stunting reduction with a prevalence target of 14% by 2024. Stunting has now become a national issue and is of particular concern to the government to overcome the risks it poses. One action that can be taken to prevent stunting is to provide intervention to families at risk of stunting. This intervention is carried out in the form of clustering of sub-districts or villages consisting of babies under two years (baduta), babies under five years (toddlers), and pregnant women with inadequate environmental aspects (sanitation and clean water). Based on this, this research aims to conduct a cluster analysis of sub-districts or villages that are at risk of stunting. The cluster analysis method uses the K-Mean algorithm with reference to 3 clusters, namely low, medium, and high. This research uses a dataset of 71 sub-districts or villages that are at risk of stunting. The research results show that the performance of the K-Means method in cluster analysis produces 32 low-risk sub-districts or villages, with a percentage of 45.07%, 36 medium risks with a percentage of 50.70%, and 3 high risk with a percentage of 4. 23%. Based on these results, this research can contribute to the relevant government in dealing with the spread of stunting

References

Apriyani, P., Dikananda, A. R., & Ali, I. (2023). Penerapan Algoritma K-Means dalam Klasterisasi Kasus Stunting Balita Desa Tegalwangi. Hello World Jurnal Ilmu Komputer, 2(1), 20–33. https://doi.org/10.56211/helloworld.v2i1.230

Lailiyah, K. (2023). PERAN BADAN KEPENDUDUKAN DAN KELUARGA BERENCANA NASIONAL DALAM PERCEPATAN PENURUNAN STUNTING . Mendapo: Journal of Administrative Law, 4(1), 16-33. https://doi.org/10.22437/mendapo.v4i1.23534

Fadilah, A., Nurfaizy P, M., Lumbanbatu, S., & Defiyanti, S. (2022). PENGELOMPOKAN KABUPATEN/KOTA DI INDONESIA BERDASARKAN FAKTOR PENYEBAB STUNTING PADA BALITA MENGGUNAKAN ALGORITMA K-MEANS. Jurnal Informatika Dan Komputer), 6(2), 223–230.

Saputro, D. T., & Sucihermayanti, W. P. (n.d.). Penerapan Klasterisasi Menggunakan K-Means untuk Menentukan Tingkat Kesehatan Bayi dan Balita di Kabupaten Bengkulu Utara. In Universitas AKI Semarang Jl Imam Bonjol (Vol. 12, Issue 2). Dadapsari, Kec. Semarang Utara.

Nasution, I., Perdana Windarto, A., & Fauzan, M. (2020). Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi. Technology and Science (BITS, 2(2), 76–83. https://www.bps.go.id.

Nyoman, I., & Adiputra, M. (2021). CLUSTERING PENYAKIT DBD PADA RUMAH SAKIT DHARMA KERTI MENGGUNAKAN ALGORITMA K-MEANS. INSERT: Information System and Emerging Technology Journal, 2(2), 99.

Utari, D. T. (2021). Analisis Karakteristik Wilayah Transmisi Covid-19 dengan Menggunakan Metode K-Means Clustering. Jurnal Media Teknik Dan Sistem Industri, 5(1), 25. https://doi.org/10.35194/jmtsi.v5i1.1220

Subayu, A. (n.d.). PENERAPAN METODE K-MEANS UNTUK ANALISIS STUNTING GIZI PADA BALITA: SYSTEMATIC REVIEW (Vol. 2). www.geospasial.com

Hutagalung, J., Ginantra, N. L. W. S. R., Bhawika, G. W., Parwita, W. G. S., Wanto, A., & Panjaitan, P. D. (2021). COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm. Journal of Physics: Conference Series, 1783(1). https://doi.org/10.1088/1742-6596/1783/1/012027

Damanik, Y. F. S. Y., Sumarno, S., Gunawan, I., Hartama, D., & Kirana, I. O. (2021). Penerapan Data Mining Untuk Pengelompokan Penyebaran Covid-19 Di Sumatera Utara Menggunakan Algoritma K-Means. Jurnal Ilmu Komputer Dan Informatika, 1(2). https://doi.org/10.54082/jiki.13

Nabila, Z., Rahman Isnain, A., & Abidin, Z. (2021). ANALISIS DATA MINING UNTUK CLUSTERING KASUS COVID-19 DI PROVINSI LAMPUNG DENGAN ALGORITMA K-MEANS. Jurnal Teknologi Dan Sistem Informasi (JTSI), 2(2), 100. http://jim.teknokrat.ac.id/index.php/JTSI

Khairunnisa, S., & Jambak, M. I. (2022). Pengelompokan Cuaca Kota Palembang Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Pola Karakteristik Cuaca. JURNAL MEDIA INFORMATIKA BUDIDARMA, 6(4), 2352. https://doi.org/10.30865/mib.v6i4.4810

Hajar, S., Novany, A. A., Windarto, A. P., Wanto, A., & Irawan, E. (2020, February). Penerapan K-Means Clustering pada ekspor minyak kelapa sawit menurut negara tujuan. In Seminar Nasional Teknologi Komputer & Sains (SAINTEKS) (Vol. 1, No. 1, pp. 314-318).

Riwayati, S., Lestari, T., Ariani, N. M., Masri, M., Kashardi, K., Ramadianti, W., & Syofiana, M. (2022). Strategi Gerakan Cegah Stunting Menggunakan Metode Penyuluhan Di Desa Lubuk Belimbing 1 Bengkulu. Journal of Empowerment, 3(1), 46.

Triyandana, G., Putri, L. A., & Umaidah, Y. (2022). Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means. Journal of Applied Informatics and Computing, 6(1), 40-46.