Klasifikasi Status Gizi Pada Balita Menggunakan Metode Naive Bayes Classifier Berbasis Web (Studi Kasus: Puskesmas Borong Kabupaten Manggarai Timur)

DOPO, Five Bunda De Putri (2024) Klasifikasi Status Gizi Pada Balita Menggunakan Metode Naive Bayes Classifier Berbasis Web (Studi Kasus: Puskesmas Borong Kabupaten Manggarai Timur). Undergraduate thesis, Universitas Katolik Widya Mandira Kupang.

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Abstract

Toddler is an abbreviation for babies under five years old, which is a period when a child's brain experiences very rapid growth. This period is also generally known as the golden age. So that in the future toddlers can grow into intelligent children, parents are obliged to provide comprehensive stimulation both in terms of health, adequate nutrition, parenting and education. The processing of toddler nutritional status data carried out by community health center staff still uses manual calculations and analysis so that the expected and obtained results are less effective in determining the nutritional status of toddlers, so a method is needed that is combined with a computer-based system that can produce the nutritional status of toddlers quickly and accurately. which can help the Borong Community Health Center in handling toddler nutrition cases. The Naïve Bayes method is a classification method that utilizes probability theory to predict future probabilities based on previous experience. 25 datasets which were divided into 20 training data and 5 testing data with classification results in each category of toddler nutritional status by producing numbers based on the nutritional status category Height According to Age (TB/U), namely 4 data predicted to be correct and 1 data predicted to be incorrect or not in accordance with real data, based on the nutritional status category Body Weight according to Age (BB/U), namely 2 data predicted to be correct and 3 data predicted wrong or not in accordance with real data, based on the nutritional status category Body Weight According to Height (BB/TB). ) namely 4 data are predicted to be correct and 1 data is predicted to be wrong or does not match the real data.

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