IMPLEMENTATION OF MACHINE LEARNING USING THE K-NEAREST NEIGHBOR CLASSIFICATION MODEL IN DIAGNOSING MALNUTRITION IN CHILDREN

The problem faced today is the lack of nutrition for children which causes stunting. One way to prevent stunting problems is to provide input to the community in Aceh for the importance of providing adequate nutrition for children. This study classifies toddlers who are identified as stunting with the K-NN model technology which is modeled in machine learning, the results are grouped. The purpose of this study was to determine the detection of malnutrition in toddlers and to classify data on malnutrition in toddlers using the k-means clustering method and the system that was built could be used as a reference to monitor the growth and development of children. Then in classifying malnutrition in children based on the results of the nutritional status criteria in toddlers, it can be known based on the index of Body Weight for Age (W/U), Height for Age (TB/U), and Weight for Height (W/TB). by entering data values ​​from weight, height and gender of toddlers. The purpose of this study was to determine the detection of malnutrition under five at the Cut Meutia Hospital Kab. North Aceh. The process in the initial data analysis of Mr. ID, baby's name, gender, age, weight (kg), height (cm), the data to be classified for training data are 40 children in each region / village. In the assessment of nutritional status, it is classified as Malnutrition less than 3 SD or 70%, Malnutrition - 3 SD to < - 2 SD or 80%, Good Nutrition -2 SD to +2 SD, Over Nutrition >+2 SD. The results of the final score obtained are euclidean distance with a value of 1.3 with a ranking of malnutrition, age 1.6 months, weight (weight) 0.852, TB (height) 4.556 with euclidean distance with a value of 1.3 with a low ranking. For the second test data, age is 2.8 months, BB (weight) 0.222, TB (height) 4.556 with Euclidean distance with a value of 1.3 with a good rating of 0.778. The results of this study can be classified in children to children for each region in each region, village and sub-district of each Puskesmas in North Aceh Regenc