Penerapan Data Mining Menggunakan Metode Algoritma K-Means Clustering Untuk Analisis Status Gizi Pada Balita Stunting (Studi Kasus: Puskesmas Kalike, Kecamatan Solor Selatan)

Werang, Maria Febriana Palin (2024) Penerapan Data Mining Menggunakan Metode Algoritma K-Means Clustering Untuk Analisis Status Gizi Pada Balita Stunting (Studi Kasus: Puskesmas Kalike, Kecamatan Solor Selatan). Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

Nutritional issues in toddlers, particularly malnutrition, remain a significant concern due to factors such as undernutrition, poor parenting practices, inadequate food intake, economic problems, infections during pregnancy, and genetic factors. This study analyzes the nutritional status of stunted toddlers at Kalike Health Center, South Solor District, using the K-Means clustering algorithm with Microsoft Excel and Orange applications for data from 2023 to 2024. K-Means clustering successfully categorized the toddler data into two clusters based on height and
weight. In 2023, Cluster 1 included toddlers with an average birth height of 48.6 cm and weight of 2.8 kg, growing to 88.1 cm and 11.5 kg at 2.3 years old. In 2024, toddlers in C1 had an average birth height of 48.7 cm and weight of 2.8 kg, with a height of 78.1 cm and weight of 9.1 kg at 1.6 years old. Cluster 2 in 2023 comprised toddlers with an average birth height of 49.1 cm and weight of 2.8 kg, growing to 63.6 cm and 6.5 kg at 1 year old. In 2024, toddlers in C2 had an average birth height of 47.6 cm and weight of 2.8 kg, with a height of 91.5 cm and weight of 11.9 kg at 3.3 years old. Toddlers in C1 generally had better height and weight compared to C2. Analysis with Orange showed the highest Silhouette Score in 2023 was 0.498, and in 2024 was 0.502, indicating good clustering quality. The K-Means Clustering method is effective for analyzing the nutritional status of stunted toddlers, and this study is
expected to aid in nutrition health programs to prevent stunting

Item Type:	Thesis (Undergraduate)
Additional Information:	Maria Febriana Palin Werang NIM : 20083000047
Uncontrolled Keywords:	Nutritional Status of Toddlers, Stunting, K-Means Clustering, Microsoft Excel, Orange
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Teknologi Informasi > S1 Sistem Informasi
Depositing User:	Gendhis Dwi Aprilia
Date Deposited:	12 Mar 2025 01:47
Last Modified:	12 Mar 2025 01:47
URI:	https://eprints.unmer.ac.id/id/eprint/4583

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