Aplikasi Naive Bayes Classifier untuk Memprediksi Status Gizi Berdasarkan Data Antropometri Santri di Pondok Pesantren Darunnajah

Abstract

Nutritional status is one of the important indicators in determining the quality of a person's health and well-being. In the Islamic boarding school environment, monitoring the nutritional status of students is crucial considering the uniform lifestyle and eating patterns. This study aims to predict the nutritional status of students at the Darunnajah Islamic Boarding School using the Naive Bayes Classifier algorithm based on anthropometric data. The data used include weight, height, and age of 250 students, which are classified into three categories of nutritional status: malnutrition, normal nutrition, and overweight. The web application is built using Python with Flask as the framework and Bootstrap to beautify the interface. The calculation of nutritional status is based on the Body Mass Index (BMI) with the formula: weight (kg) divided by height (m) squared. The results of the study showed that the Naive Bayes Classifier prediction model has a good level of accuracy in classifying the nutritional status of students.