CAUSAL MODELING OF FACTORS IN STUNTING USING THE PETER-CLARK AND GREEDY EQUIVALENCE SEARCH ALGORITHMS

Stunting is one of the nutritional problems that can hinder the growth and development process in toddlers. Untreated stunting can lead to fatal outcomes. Previous research on the factors that exist in the incidence of stunting mostly used multivariate analysis. Previous research on stunting factors has primarily used multivariate or correlation analyses. However, this study uniquely focuses on establishing causal relationships between these factors, a crucial step in improving early diagnosis for stunting prevention and treatment. The data used in this research was 83 data on stunting incidents and consisted of eight parameters. The purpose of this study is to model the causal relationship between factors that represent the incidence of stunting. This study uses two simple causal approaches, namely the Peter-Clark (PC) algorithm to obtain the initial concept of a graph model of the relationship between variables and the Greedy Equivalence Search (GES) algorithm to refine the model by obtaining the direction of the causal relationship. There are six bi-directed relationships that have been found, namely from food variables to support; maternal knowledge with sanitation; Height/Age and Weight/Age with Child Nutrition; height/age with weight/age and stunting. In addition, both algorithms in this study have successfully obtained a causal model, by comparing performance using directional and causal densities that the GES algorithm was able to identify a relationship of 0.66 compared to the PC algorithm.