Klasifikasi Gizi Balita Untuk Deteksi Dini Stunting Menggunakan Metode KNearest Neighbor

Naufal Hafiz Farhan (Universitas Sangga Buana YPKP Bandung, Indonesia)
Gunawansyah Gunawansyah (Universitas Sangga Buana YPKP Bandung, Indonesia)

G-Tech : Jurnal Teknologi Terapan
Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024

The nutritional status of toddlers is an important indicator reflecting the welfare level of society. In Indonesia, the prevalence of toddlers with poor nutrition and stunting remains a significant issue that requires special attention. This study aims to develop a toddler nutritional status classification system using the K-Nearest Neighbor (KNN) method as a tool for early stunting detection. The KNN method was chosen due to its ability to classify data based on proximity. The data used in this study were obtained from the Cileunyi Health Center, with anthropometric parameters including Age, Weight, and Height. The system was implemented using the PHP programming language and MySQL database. The test results showed that the system achieved an accuracy of 66.67% for Weight-for-Age (W/A) classification and 100% for Height-for-Age (H/A) classification. Thus, this system is expected to be an effective tool for healthcare professionals in the early detection of stunting in toddlers, allowing for timely and appropriate interventions.