Prediction of linear model on stunting prevalence with machine learning approach

Autor:	Mambang Mambang, Finki Dona Marleny, Muhammad Zulfadhilah
Rok vydání:	2023
Předmět:	Control and Optimization Computer Networks and Communications Hardware and Architecture Control and Systems Engineering Computer Science (miscellaneous) Electrical and Electronic Engineering Instrumentation Information Systems
Zdroj:	Bulletin of Electrical Engineering and Informatics. 12:483-492
ISSN:	2302-9285 2089-3191
Popis:	An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9b2fd26223ba621f2e1892f691ec0e23 https://doi.org/10.11591/eei.v12i1.4028 Zobrazit plný text záznamu