Aplikasi Berbasis Website Untuk Mendeteksi Status Gizi Balita Menggunakan Metode K-Nearest Neighbors (KNN) - Ritonga, Alven Safik; Muhandhis, Isnaini (Journal of System and Computer Engineering, 2024)
Detecting the nutritional status of toddlers is important in monitoring and caring for toddlers' health. In this study, we propose the use of the K-Nearest Neighbors (KNN) method to detect the nutritional status of toddlers based on relevant attributes such as weight, height, age, gender and nutritional intake. This research involves developing a computer-based application that uses the KNN algorithm to classify the nutritional status of toddlers. Toddler data that has been collected from legitimate sources is used to train the KNN model. After training, this model can predict the nutritional status of new toddlers based on the entered attributes. In our experiments, we test and evaluate the performance of KNN models using evaluation metrics such as accuracy, precision, recall, and F1-score. In practical applications, the KNN model can be used as an aid in determining the nutritional status of toddlers and providing recommendations for appropriate action, such as increasing nutritional intake or necessary medical care. This research makes a contribution to the field of monitoring and caring for toddler nutrition by combining the KNN method as a tool for detecting nutritional status. The application developed can help medical personnel and parents monitor and take appropriate action related to toddler nutrition. The application, which has been built in the form of a website, can help detect the nutritional status of toddlers. When applying the KNN method to toddler nutritional status data, the application was successful in detecting toddler nutritional status with an accuracy of 74.73%.