Application of Apriori Algorithm in Data Mining to Analyze Malnutrition (Case Study: Secanggang Village) - Sulistianingsih, Indri; Fitriani, Wirda; Nasution, Darmeli (International Conference on Sciences Development and Technology, 2023)
Malnutrition is a serious public health issue affecting quality of life in many rural areas. Comprehensive, evidence-based approaches are needed to identify dietary patterns associated with this condition. In this study, we applied the Apriori algorithm for data mining to analyze the relationship between dietary patterns and malnutrition prevalence in Secanggang Village. Using Apriori, we discovered significant associative patterns between foods and malnutrition. Our analysis showed several dietary patterns with sufficiently high support and confidence, indicating potentially strong associations with malnutrition. These patterns may include combinations of less diverse or nutritious foods, which could be risk factors for malnutrition. Our findings provide valuable insights for authorities and health institutions to formulate more effective intervention programs in tackling malnutrition in Secanggang Village. By understanding the relationship between dietary patterns and malnutrition, prevention and treatment efforts can be better targeted to positively impact community health and welfare. Further research and cross-sectoral collaboration are still needed to comprehensively address the complex challenges of malnutrition.