PENERAPAN GENERALIZED LINEAR MODEL DALAM MENANGANI OVERDISPERSI PADA DATA PENGANGGURAN DI INDONESIA

Abstract

Unemployment remains a major problem in Indonesia. In 2023, the open unemployment rate was recorded at 5.32%, or approximately 7.86 million people. Low absorption of productive-age workers and limited job opportunities have led to a potential increase in unemployment in several regions. This situation requires in-depth analysis to reduce the increase in the open unemployment rate. Various socio-economic factors in Indonesia influence the unemployment rate and can be analyzed using the Generalized Linear Model (GLM) framework. Because unemployment data is count data, the approach used is Poisson Regression. This model has the basic assumption of equidispersion, meaning that the variance is equal to the mean. However, observations indicate that the variance is greater than the mean, resulting in overdispersion. To address this, a GLM development with an additional dispersion parameter, namely Poisson Generalized Inverse Gaussian Regression (PGIGR), was used. This model was chosen because it can capture greater data variation and represent factors that influence the unemployment rate. The results of this study indicate that the number of unemployed between provinces in Indonesia is influenced by the variables of Provincial Minimum Wage, GRDP Growth Rate on a Constant Basis, Literacy Rate, and TPAK, with an AIC value of 33,577.45.