Pengelompokan Status Stunting Pada Anak Menggunakan Metode K-Means Clustering

 (*)Intan Saleha Tinendung Mail (Universitas Islam Negeri Sumatera Utara, Medan, Indonesia)
 Ilka Zufria (Universitas Islam Negeri Sumatera Utara, Medan, Indonesia)

(*) Corresponding Author

Submitted: October 13, 2023; Published: October 28, 2023

Abstract

Stunting is a physical health disorder caused by a deficiency or imbalance of nutrients necessary for the growth and development of children. This article examines the problem of stunting in children in Kerajaan sub-district, with a focus on the Sukaramai village of Pakpak Bharat district. In 2021, Pakpak Bharat District saw an increase in the number of toddlers in North Sumatra, who were diagnosed with stunting, reaching 21.25%. This occurs due to socio-economic factors and socio-cultural background which have a lot to do with diet and nutrition. According to the Indonesian Toddler Nutrition Statutes (SSGBI), in 2019, the stunting rate in Indonesia increased to 27.7%. The impact of stunting on children includes physical growth disorders, delayed brain development, and the risk of chronic disease in adulthood. Recognizing the urgency of this problem, the government has taken various steps, including the designation of funds for stunt prevention programs. This study uses the K-Means Clustering method to group stunting status in children into three categories: normal, stunting, and rapid growth. Therefore, a method is needed to group stunting status in children, namely using the Clustering method with the K-Means algorithm. The aim is to assist the government in adopting appropriate policies related to reducing the prevalence of stunting in children based on the status and problems of each cluster. The data used primarily comes from the Sukaramai village. The results of the research showed that around 30% of the 101 children studied experienced stunting in the Kerajaan sub-district, including 0 total, 43 children with normal status, 1 total, 31 children with stunting, and 2 total children. 27 children who had not yet developed rapidly did not start early his umur. This study makes an important contribution to the management of data on the nutritional status of children in the remote area and can be a reference for future research. By applying the K-Means Clustelring algorithm, this study helps understand stunting patterns and design more targeted solutions.

Keywords


Clulstelring; Data Mining; Stulnting; K-Melans; Python

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