Segmentation of stunting, wasting, and underweight in Southeast Sulawesi using geographically weighted multivariate Poisson regression.

The health profile of Southeast Sulawesi Province in 2021 shows that the prevalence of stunting is 11.69 %, wasting 5.89 % and underweight 7.67 %. This relatively high figure should be immediately reduced to zero because it greatly affects the quality of human resources. Cases of stunting, wasting and underweight are an iceberg phenomenon, especially in Southeast Sulawesi. Therefore, it is necessary to research the number of cases of stunting, wasting and underweight in Southeast Sulawesi using GWMPR. The research results show that there is a trivariate correlation between the number of cases of stunting, wasting and underweight. The GWMPR model provides better results in modeling the number of stunting, wasting and underweight cases than the MPR model. The models produced for each sub-district are different from each other based on the predictor variables that have a significant effect and the estimated parameter values ​​for each sub-district. The segmentation of the number of stunting cases consists of 21 regional groups with 10 significant predictor variables, while the number of wasting cases consists of 10 regional groups with 9 significant predictor variables, while the number of underweight cases consists of 37 regional groups with 11 significant predictor variables. Therefore, policies on stunting, wasting, and underweight should be based on local conditions. 3 important components of this study: 1. GWMPR is the development of GWPR model when there are 2 or more response variables that are correlated. 2. GWMPR is a spatial model that considers geography. 3. Application of GWMPR to the analysis of the number of stunting, wasting, and underweight in Southeast Sulawesi province.

Segmentation of toddler nutritional status using REBUS and FIMIX partial least square in Southeast Sulawesi.

Nutrition is one of the important factors that play a major role in the growth and development of children so that they can develop optimally. Child malnutrition, such as stunting, underweight, and wasting, is a significant problem in Indonesia. The World Health Organization (WHO) determined that the nutritional status of children under five in Indonesia is in the chronic category, one of which is in Southeast Sulawesi Province. This study examines and analyzes the factors that influence the nutritional status of children under five in Southeast Sulawesi using the Partial Least Square Structural Equation Modeling (SEM-PLS) method and then segments the nutritional status of children under five using Response Based Unit Segmentation Modeling in Partial Least Square (REBUS-PLS) and Finite Mixture Partial Least Square (FIMIX-PLS). The number of observations in this study was 216 sub-districts. From the results of the SEM-PLS analysis conducted, it was concluded that the 10 indicators used were valid and significant in describing the latent variables, and the practice factor variable had an effect on the food factor variable, the food factor variable had an effect on the service factor variable, and the service factor variable had an effect on the under-five nutritional status variable. The REBUS-PLS analysis results in two segments, with one segment of 75 observations and the other segment of 141 observations. The same conclusion is obtained as in the SEM-PLS analysis, but the results of the analysis with REBUS-PLS have a greater value than the results of the SEM-PLS analysis. Key points of the article: 1.Comparing REBUS-PLS and FIMIX-PLS methods for overcoming the case of heterogeneity in dat.2.Combining the SEM-PLS method with the REBUS and FIMIX methods in discussing the factors that influence the nutritional status of children under five in Southeast Sulawesi.

Determination of the best multivariate adaptive geographically weighted generalized Poisson regression splines model employing generalized cross-validation in dengue fever cases.

This article constructs a new model based on multivariate adaptive generalized Poisson regression splines (MAGPRS) and geographically weighted generalized Poisson regression (GWGPR), which is known as multivariate adaptive geographically weighted generalized Poisson regression splines (MAGWGPRS). The article elaborates the steps of weighted maximum likelihood estimation (weighted-MLE) to obtain the estimated values of its parameters. MAGWGPRS and MAGPRS were applied to the number of dengue hemorrhagic fever (DHF) cases in 119 districts or cities in Java, Indonesia, in 2020, to compare their performance. The fitted value plot versus actual data and a comparison of the mean square error (MSE) value demonstrate the goodness of the two models. The best MAGWGPRS model for each location was obtained, and only one the best MAGPRS model for all locations was acquired. Based on the plot results of the fitted value with the actual data and MSE value, MAGWGPRS is determined to be superior to MAGPRS.