INTEGRATION CLUSTER AND PATH ANALYSIS BASED ON SCIENCE DATA IN REVEALING STUNTING INCIDENTS

The purpose of this study is to applied big data to explore the factors that influence the prevalence of stunting in Wajak, to model these factors using cluster analysis models and integrated path analysis, and to develop an information system that can provide comprehensive information about stunting in the Wajak. This study uses a descriptive and explanative approach, namely using Discourse Network Analysis, cluster analysis, path analysis, and integration of cluster and path analysis. The sample of this research is children under five in Wajak District who were selected using stratified random sampling. The distance measure that has the highest model goodness vsquaredue R²in modeling using the integration of cluster analysis with path analysis is the Mahalanobis distance measure. The cluster analysis with Mahalanobis distance produces 3 clusters where cluster one is a toddler who has a low stunting category, cluster two is a group of toddlers who has a moderate stunting category, and cluster three is a group of toddlers who has a high stunting category. The originality of this study is the application of Discourse Network Analysis to obtain new variables followed by a comparison of three distances namely euclidean, manhattan, and mahalanobis in modeling using cluster integration and parametric paths.