Forecasting of Poverty using the Ensemble Learning Classification Methods

Poverty is a social-cultural problem that can be categorized into monetary approach, capability social exclusion, and participatory poverty assessment. However, the existing measurement methods are complex, costly, time-consuming. This research was conducted to forecast using classification methods. Random Forest Extreme Gradient Boosting (XGBoost) algorithms were applied since they supervised learning use ensemble approach for classification. Ensemble Learning has improved of obtained better predictive performance. The results showed trend, which helped determine Hence, this method will help government act produce specific plan reduce rate. It strategic move global poverty, parallel Goal 1 Sustainable Development (SDG): No