PENERAPAN DATA MINING UNTUK ANALISIS KESEHATAN BALITA MENGGUNAKAN METODE K-MEANS DAN DECISION TREE (STUDI KASUS PUSKESMAS PAAL LIMA): Data Mining, Health Toddlers, Nutritional Status, K-Means, Decision Tree - Limia Budiarti, Rike; Mulyati, Sri; Zaidan, Ahmad (FORTECH (Journal of Information Technology), 2025)
Advances in information technology provide opportunities in health data analysis, particularly in the nutritional status of toddlers. This study applies data mining methods using the K-Means and Decision Tree algorithms to quickly and accurately group and classify the nutritional status of toddlers. K-Means is used to group data based on age, weight, and height into categories of good, poor, and very poor nutrition. The clustering results are then fed into the Decision Tree, which generates data-based nutritional intervention recommendations. This study used a quantitative approach with Orange Tools and a dataset of toddlers from the Paal Lima Community Health Center in Jambi City. The test results showed a classification accuracy of 92.3% and a 30% increase in nutritional analysis efficiency compared to the manual method. Thus, the application of data mining has proven to be effective in supporting community health centers in decision-making and improving toddler health services.