Implementation of Improve Apriori Algorithm for Families at Risk of Stunting

Abstract

Stunting is a serious health issue in Indonesia, particularly among families with low socio-economic conditions. However, the lack of precise criteria or measurements of social conditions contributing to at-risk families makes prediction challenging. This study aims to identify patterns of relationships among 17 criteria influencing stunting risk, such as maternal age, number of children, type of flooring in the house, and access to clean water, by enhancing the efficiency of the Apriori algorithm through hash-based techniques. Data were obtained from families in Tuah Madani District, Pekanbaru, and analyzed using data preprocessing and transformation methods. The implementation of this algorithm within a web-based information system enables rapid and efficient analysis to identify stunting risks based on relevant combinations of criteria. The analysis results indicate that certain criteria, such as maternal age above 35 years, status as a couple of childbearing age (PUS), and having more than three children, are significantly associated with stunting risk, with a support value of 37.54% and a confidence level of 83.16%. This study contributes to the development of efficient methods for stunting risk analysis and provides a foundation for more targeted health interventions. Future researchers are advised to expand the data scope by including additional regions and different time periods to improve result generalization. Furthermore, incorporating other variables, such as maternal nutritional status or the education level of household heads, may offer deeper insights into understanding stunting risk patterns.