KLASIFIKASI STATUS GIZI BAYI MENGGUNAKAN METODE K-NEAREST NEIGHBOR - Sri Ayu Lestari; Natalis Ransi; Muhammad Arfan (Program Studi Ilmu Komputer FMIPA-UHO, 2025)
The development of a baby nutritional status classification system was carried out by applying the K-Nearest Neighbor (KNN) method with the aim of supporting more accurate monitoring of infant growth and development. The system determines the nutritional status of infants based on input data including age, weight, height, and mid-upper arm circumference, which are then compared with available training data. The system development process employed the waterfall approach, encompassing requirements analysis, system design, implementation, and testing stages. Testing was conducted using black box testing to ensure that all system functions operated according to requirements, as well as a confusion matrix to measure classification accuracy. The results showed that all system features functioned properly and the achieved accuracy rate reached 97.30%, indicating that the system has very good performance in effectively supporting the monitoring of infant nutritional status.