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Analisis Perkembangan Gizi Balita Stunting Di Kota Blitar Melalui Data Mining Dengan Algoritma K-Means Clustering Menggunakan Orange

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Putri, Dini Kurnia (2023) Analisis Perkembangan Gizi Balita Stunting Di Kota Blitar Melalui Data Mining Dengan Algoritma K-Means Clustering Menggunakan Orange. Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

Good growth and development of early childhood is one of the factors supporting the country's progress which requires special attention from the government and parental knowledge about malnutrition in early childhood (stunting). Data from the Central Statistics Agency (BPS) shows that the prevalence of malnourished children under five years old was more than 10 in 2018 and the value for East Java was 15.20. With technology currently developing very rapidly, and taking into account the cases above, a system can be created that can help analyze the nutritional status of toddlers using several mass methods which are intended to facilitate the analysis process. The analysis technique used is grouping or Clustering where this method can be used to classify and sort data so that decisions can be made regarding the phenomenon of stunting malnutrition. K- Means Clustering is a method of analyzing data in Data Mining where the modeling process is unsupervised and the method is to group the data in partitions. From research regarding nutritional data for toddlers in Blitar City which was carried out at the Sukorejo Community Health Center and the processing process to obtain a list of toddlers from year to year who are needed (the past 5 years) with contents regarding Age, Height (TB), and Body Weight (BB) of toddlers aged 1 month to 5 years old. Produces 4 Centroids as follows: level of malnutrition, stunting, malnutrition, moderate nutrition, good nutrition. Which aims to determine the clustering of stunting among toddlers in Blitar City.

Item Type: Thesis (Undergraduate)
Additional Information: Dini Kurnia Putri NIM : 19083000114
Uncontrolled Keywords: Stunting, Data Mining, Nutrition, K-Means Cluster
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Teknologi Informasi > S1 Sistem Informasi
Depositing User: nata Natassa Auditasi
Date Deposited: 09 Jul 2025 08:50
Last Modified: 09 Jul 2025 08:50
URI: https://eprints.unmer.ac.id/id/eprint/5440

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