Analysis of Stunting Factors in Toddlers Using Data Mining Method the Apriori Algorithm

Abstract

Stunting is one of the developmental problems in children which is influenced by many factors. Experts of nutrition mention several influencing factors such as nutrition during pregnancy, the mother's knowledge about nutrition, limited access to services, and inadequate access to sanitation and water hygiene. These patterns will be analyzed using a data mining method. In this study, 557 stunting data in Jakarta will be analyzed. The data mining method used is the association rule with the a priori algorithm. The steps taken are to perform data cleaning, data integration, data transformation, data mining, and pattern evaluation. There are many attributes in the data studied, but it is determined that several attributes are appropriate to obtain optimal results. The attributes taken are adjusted to research from the health sector. The test results produce several association rules. From all the association rules obtained using the Apriori algorithm, it is taken that meets a minimum support of 0.2 and a minimum confidence of 0.5. There is a result of 73% of the total meeting the minimum support and confidence. So that the results of the association rules can be used by health experts and stakeholders as a reference in determining policy.