One of the problems faced by the Province of West Java is poverty. The response data used in this study is the percentage of the poor who have a non-normal distribution. Fixed effects variables consist of life expectancy, school year expectancy, and school enrollment rates aged 16-18 years. Years and clusters variables will be included as independent variables but are random effects. Therefore, for this data case, the appropriate model to be developed is Binomial GLMM and Beta-Binomial HGLM. The modeling performed on the percentage of poor population data gives relatively the same results between the model approaches using Binomial GLMM and Beta-Binomial HGLM with a random effect of years. The random effect in both models shows that there is a variation in the percentage of poor people in years, but the variabilitybetween years cannot be shown by the data. Likewise, the modeling carried out on the percentage of poor population data gives relatively the same results between the model approaches using Binomial GLMM and Beta-Binomial HGLM with clusters random effect. Fixed effects variables and random effects have a significant effect on the percentage of poor people for all models. The model obtained by involving all fixed effects and random effects (clusters) is the best model because itprovides the smallest CAIC value compared to models using random effects (Years).

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