Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City

Authors

  • Ozzi ardhiyanto Bhayangkara University
  • Muhammad Salam Asyidqi
  • Ajif Yunizar Pratama Yusuf, S.Si, M.Eng
  • Dr. Tb. Ai Munandar, S.Kom., MT

DOI:

https://doi.org/10.58776/ijitcsa.v1i3.42

Keywords:

Clustering, Infant nutrition, Agglomerative Clustering, Malnutrition, Nutritional grouping

Abstract

Clustering infant nutrition based on weight, height, and age is a data analysis method used to group infant nutritional status based on these characteristics. The research on clustering infant nutrition aims to analyze whether there are still many infants in the area with insufficient or excessive nutrition, and to identify groups of infants requiring special attention regarding their nutritional intake. In the analysis of infant nutrition clustering, data on weight, height, and age of infants are collected and then grouped based on similarities in body height and weight at certain ages. The method used in this research is hierarchical clustering, which can help in grouping the data. Clustering analysis can help understand how infants' feeding patterns vary based on their weight, height, and age. The results of research on clustering infant nutrition based on weight, height, and age can provide valuable insights for nutrition experts, pediatricians, and community health workers in developing appropriate intervention programs to improve infant feeding patterns and meet their nutritional needs. Additionally, the results of clustering infant nutrition can also be used to identify groups of infants requiring special attention regarding their nutritional needs, thus minimizing the risk of malnutrition and unhealthy growth in infants.

References

A. Ayu Fitriana STKIP Modern Ngawi Jl Ir Soekarno Kabupaten Ngawi, “Fitriana, Sumiatun: Pemahaman Orang Tua Mengenai Gizi Anak Pemahaman Orang Tua Mengenai Gizi Anak.”

A. Audilla, Stik. Hutama Abdi Husada, and K. Penulis, “Jurnal Ilmiah Pamenang-JIP HUBUNGAN PENGETAHUAN IBU TENTANG NUTRISI DENGAN STATUS GIZI BALITA DI POSYANDU DESA WAJAK LOR KECAMATAN BOYOLANGU KABUPATEN TULUNGAGUNG TAHUN 2022,” vol. 4, no. 1, pp. 22–26, 2022, doi: 10.53599.

U. Jenderal, A. Yani, and Y. Abstrak, “ANTROPOMETRI PENGUKURAN STATUS GIZI ANAK USIA 24-60 BULAN DI KELURAHAN BENER KOTA YOGYAKARTA Reni Merta Kusuma, Rizki Awalunisa Hasanah,” 2018.

G. Tapanuli, U. Oleh, D. W. Sitohang, and A. Rikki, “Pulo Brayan Darat I, Kec,” 2019.

G. Abdurrahman, “Clustering Data Kredit Bank Menggunakan Algoritma Agglomerative Hierarchical Clustering Average Linkage”.

S. Gizi Balita Di Kelurahan Jumput Rejo Sukodono Sidoarjo Amir Ali, R. Medis dan Informasi Kesehatan Stikes Yayasan Rumah Sakit DrSoetomo DrMoestopo, and A. Surabaya, “Clustering Data Antropometri Balita Untuk Menentukan,” 2020. [Online]. Available: http://jurnal.mdp.ac.id

H. Februariyanti and D. B. Santoso, HIERARCHICAL AGGLOMERATIVE CLUSTERING UNTUK PENGELOMPOKAN SKRIPSI MAHASISWA. 2017.

E. Widodo, P. Ermayani, L. N. Laila, and A. T. Madani, “Pengelompokkan Provinsi di Indonesia Berdasarkan Tingkat Kemiskinan Menggunakan Analisis Hierarchical Agglomerative Clustering (Indonesian Province Grouping Based on Poverty Level Using Hierarchical Agglomerative Clustering Analysis).”

L. Zahrotun, “ANALISIS PENGELOMPOKAN JUMLAH PENUMPANG BUS TRANS JOGJA MENGGUNAKAN METODE CLUSTERING K-MEANS DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING (AHC),” 2015.

H. Februariyanti et al., “I N F O R M A T I K A ANALISIS KECENDERUNGAN INFORMASI MENGGUNAKAN ALGORITMA HIERARCHICAL AGGLOMERATIVE CLUSTERING,” Jurnal Informatika, Manajemen dan Komputer, vol. 13, no. 1, 2021.

T. Regina, A. W. Program, S. Keuangan, and P. P. Swadharma, “TATA KELOLA PEMERIKSAAN GIZI BAYI, BALITA DAN USIA LANJUT.”

D. P. Lestari, “Upaya Pencegahan Risiko Gizi Buruk pada Balita: Literature Review,” Jurnal Ilmiah Universitas Batanghari Jambi, vol. 22, no. 1, p. 532, Mar. 2022, doi: 10.33087/jiubj.v22i1.1828.

S. Indah Nurhafida and F. Sembiring, “ANALISIS TEXT CLUSTERING MASYARAKAT DI TWITER MENGENAI MCDONALD’SXBTS MENGGUNAKAN ORANGE DATA MINING,” 2021.

R. P. Justitia, N. Hidayat, and E. Santoso, “Implementasi Metode Agglomerative Hierarchical Clustering Pada Segmentasi Pelanggan Barbershop (Studi Kasus : RichDjoe Barbershop Malang),” 2021. [Online]. Available: http://j-ptiik.ub.ac.id

R. T. Aldisa, “JURNAL MEDIA INFORMATIKA BUDIDARMA Data Mining Penentuan Jurusan Siswa Menggunakan Metode Agglomerative Hierarchical Clustering (AHC),” 2023, doi: 10.30865/mib.v7i2.6092.

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Published

10-09-2023

How to Cite

Ozzi ardhiyanto, Muhammad Salam Asyidqi, Ajif Yunizar Pratama Yusuf, S.Si, M.Eng, & Dr. Tb. Ai Munandar, S.Kom., MT. (2023). Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City . International Journal of Information Technology and Computer Science Applications, 1(3), 122–128. https://doi.org/10.58776/ijitcsa.v1i3.42

Issue

Vol. 1 No. 3 (2023): September - December 2023

Section

New Submission

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Copyright (c) 2023 Ozzi ardhiyanto, Muhammad Salam Asyidqi, Ajif Yunizar Pratama Yusuf, S.Si, M.Eng, Dr. Tb. Ai Munandar, S.Kom., MT

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