APPLICATION OF MACHINE LEARNING IN PREDICTING CHILDREN'S NUTRITIONAL STATUS WITH MULTIPLE LINEAR REGRESSION MODELS - Mutammimul Ula; Ananda Faridhatul Ulva; Mauliza; Muhammad Abdullah Ali; Yumna Rilasmi Said (Universitas Mulia, 2022)
Forecasting is an important part of making plans and making decisions that can predict future events. Forecasting techniques in this study used multiple linear regression. This study aims to predict the number of cases of child nutritional status in children in each region. The purpose of this study was to see the results of predicting the number of children's nutritional status in each region and to make it easier to predict children's nutrition. The research method includes the analysis of the system built and the design of machine learning applications using the Multiple Linear Regression method. Then the system built can help predict the nutritional status of children in Aceh quickly, precisely, and accurately. The data used is data on the nutritional status of children in 2018, 2019, and 2020. Based on the results of forecasting for 2021 based on data obtained in previous years, the predicted results of total nutritional status in 2021 are 449,0912126. The results of this study indicate that the linear regression method obtains the best model results by being able to predict the implementation of machine learning.