PENERAPAN ALGORITMA K-NEAREST NEIGHBORS DAN ARTIFICIAL NEURAL NETWORK PADA KLASIFIKASI STATUS GIZI BALITA BERDASARKAN INDEKS ANTROPOMETRI

Health problems related to nutritional status is still a big deal in Indonesia. Data from the survey on the nutritional status of toddlers in Indonesia (SSGBI) in 2021 shows that the prevalence of stunting in Indonesia has reached 24.4%, wasted has reached 7.1%, and underweight reaching 17.0%. The number of toddlers suffering from stunting in Indonesia still exceeds the threshold set by WHO, which is 20%. Even though it is decreasing every year, the problem of malnutrition in Indonesia is still high. Therefore, recording and grouping nutrition under five to determine the growth and development and nutrition of children under five in order to reduce the level of malnutrition becomes very important. One way to group data is by classification. In this study, classification was carried out using the K-Nearest Neighbor algorithm and an Artificial Neural Network. The K-Nearest Neighbors algorithm is an algorithm for classifying based on the proximity of a data location (distance) to other data. Meanwhile, the ANN algorithm is a computational system algorithm where the architecture and operations are inspired by knowledge of biological nerve cells in the brain. Assessment of the nutritional status of toddlers can be measured based on anthropometric measurements consisting of the variables age, sex, weight (BB) and height (TB). The results showed that by dividing the data into 80% training data and 20% testing data, it was obtained that the ANN algorithm with a learning rate of 0.05 and the k-NN algorithm with k = 3 had the most optimum accuracy value (99%). The model is saved and loaded into a web app with 3 nutritional status categories, namely Weight/Age, Weight/Height, and Height/Age.

Keywords: Nutritional Status, Classification, KNN, Artificial Neural network