Modeling Classification Of Stunting Toddler Height Using Bayesian Binary Quantile Regression With Penalized Lasso

Abstract

Stunting is a child who has a height that is shorter than the age standard. One of the main indicators of stunting is a height that is lower than the standard for toddlers. Stunting in Indonesia is of great concern due to the high prevalence of stunting. Stunting children are at risk of impaired cognitive development, which will result in the development of human resources. This study aims to develop a classification model to detect stunted toddlers based on height using the Bayesian binary quantile regression method with LASSO (Least Absolute Shrinkage and Selection Operator). This method was chosen because of its ability to handle multicollinearity and variable selection problems automatically, as well as provide better estimates on non-normally distributed data. The data used in this study includes five independent variables such as age, weight at birth, gender, how to measure height and nutritional status. The results showed that independent variables that significantly affect the height of stunting toddlers can be a concern to reduce the problem of stunting in Indonesia. The results of model show that variable age, weight at birth, and nutritional status have a significant influence to classification of stunting toddler height. Indicator of model goodness is seen from the quantile that has the smallest MSE value. The model that has the smallest MSE is in quantile 0.25 with an MSE value of 0.1622.