IMPLEMENTASI MACHINE LEARNING MENGGUNAKAN METODE CASE BASED REASONING UNTUK DIAGNOSA GIZI BURUK PADA ANAK

Abstract

Malnutrition is a problem experienced by children in Indonesia, one of which is stunting. The role of Community Health Center is needed in overcoming malnutrition rates in toddlers. This research was conducted by implementing machine learning using the case-based reasoning method to diagnose malnutrition in children. This research aims to reduce stunting experienced by the community around Bengkayang Regency. The purpose of this study was to determine the diagnosis of malnutrition in children at the Bengkayang Health Center. The output of this study is to be able to diagnose malnutrition in children using the case-based reasoning method and the system designed is used as a reference for addressing child development. The variables used in implementing the system are name, age, gender, height, and weight. Then the learning machine looks for the closest case to see the value closest to the stunting problem, so the result is the same. In solving cases by calculating the similarity value, it was found that the new case had similarities with case 04 which was diagnosed with Scurvy Vitamin Deficiency with a similarity value of 0.545454545 or 54.54%.