Analisis Regresi Kernel Gaussian untuk Memprediksi Indeks Pembangunan Manusia (IPM) Berdasarkan Faktor Sosial-Ekonomi Provinsi di Indonesia

Abstract

The Human Development Index (HDI) functions as a key indicator for assessing the level of welfare and overall quality of life of the population within a specific region. This study aims to examine the socio-economic factors influencing HDI at the provincial level in Indonesia using a Gaussian kernel regression approach. A nonparametric method is employed due to its flexibility in capturing nonlinear relationships between the response and predictor variables without the need to assume a specific functional form. The analysis utilizes secondary data, including education, poverty, per capita expenditure, expected years of schooling, open unemployment rate, and gross regional domestic product for each Indonesian province. The findings from this study indicate that educational factors, particularly mean years of schooling and expected years of schooling, exert the most significant impact on HDI improvement. The estimated Gaussian kernel regression model demonstrates a coefficient of determination of 0.9954 and a residual standard error of 0.3468, reflecting a very high predictive accuracy and relatively low error. These results suggest that Gaussian kernel regression is an effective nonparametric approach for analyzing human development in Indonesia.