RANCANG BANGUN SISTEM KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN)

Abstract

Children under five age is a group that susceptible to health problems such as lack of energy and protein nutrition, so this age group should get special attentions. One of the problems that should get an attention is problems nutritional status of children. Nutritional status of children is one of the indicators the level of social welfare. The classification of children’s nutritional status was conducted by nutritionist, but the problem is the scattering of nutritionist in Palu is very limited, especially in areas which far away from the city center. This case of study will be taken from Pantoloan Boya village. The limited of nutritionist was being the problems in detecting the indication of malnutrition. Through this research will be made an implementation based of computer system that has a same understanding as a nutritionist who is able to determine the nutritional status of children. One method that can be used in solving the classification of the nutritional status of children is K-Nearest Neighbor (KNN) method. K-Nearest Neighbor (KNN) is one method that use the learning algorithm where the result from the new testing sample is classified based on the majority of KNN’s category. In this research, the system classified the children according to their nutritional status based on data that obtained from the place of research. These results using k = 1 as the number of nearest neighbors which labels the majority of the k nearest neighbors are used to predict the unknown nutritional status of new data. This is because for k = 1 has better accuracy results than other values ​​of k is equal to 81.67%.