Prediksi Tingkat Kesejahteraan di Indonesia Menggunakan Random Forest Regressor Berdasarkan Indikator SDGs

Abstract

Every country in the world has a responsibility to ensure the welfare of its people. The welfare of the people in Indonesia can also be assessed from the high and low HDI value. As an effort to improve people's welfare and overcome social inequality, Indonesia has committed to achieving the Sustainable Development Goals (SDGs) that have been inaugurated by the United Nations (UN). The development of effective and sophisticated analysis methods is a must to know and control risks related to people's welfare. One of the approaches that emerged is the application of Machine Learning techniques, especially Random Forest Regressor which is one of the ensemble learning based algorithms implemented for regression tasks. From the entire process of this research, in the prediction of community welfare in Indonesia using Random Forest Regressor based on SDGs indicators, MSE value of 0.96, RMSE of 0.92 and R2 of 0.96.