Klasifikasi Status Stunting Balita Menggunakan Metode Naïve Bayes Gaussian Berbasis Web
DOI:
https://doi.org/10.32493/informatika.v8i3.33399Keywords:
Toddler, Stunting, Data Mining, Classification, Naïve BayesAbstract
The growth and development of toddlers must get attention from parents because toddlerhood is a golden period in shaping the growth and development and intelligence of children. Stunting is a state of malnutrition in which stunted growth and development of children and this is included in chronic nutritional problems, the incidence of stunting can be seen from height that is not in accordance with age. In preventing toddlers from stunting, it is necessary to anticipate early prevention by conducting examinations at the nearest posyandu which is measured using anthropometric methods. The calculation of stunting or normal status based on anthropometric data is generally processed manually so that there is a high possibility of errors in calculating and entering data. Data mining can make classifications or predictions on the stunting status of toddlers by studying previous data patterns. Naïve bayes is one classification method that has the advantage of high accuracy with little training data as for the attributes used in this study, namely age, gender, Early Initiation of Breastfeeding (IMD), weight, height. Based on the test results, the best average accuracy was obtained on numerical data types for age, weight, height and nominal gender attributes, Early Breastfeeding Initiation (IMD) with the highest accuracy in the 80:20 data comparison, which is 80.34% with a total of 1172 data.
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