Modeling of Educated Unemployment Rates in Indonesia Using Geographically Weighted Regression

Abdilla Mufarida, Muhammad Marizal

Abstract


Modeling the Educated Unemployment Rate (EUR) in Indonesia is an effort to reduce the number of unemployed, especially the unemployed who are able to complete their education at least high school. The SATKERNAS survey stated that more than half of the unemployed in Indonesia in 2022 were educated unemployed. Modeling was generally carried out to see the relationship between independent variables and dependent variables using linear regression analysis and specifically using Geographically Weighted Regression (GWR) analysis to understand significant differences in independent variables between provinces in Indonesia. In accordance with the model goodness criteria, GWR produces a model that has a smaller AIC value than the general model generated by linear regression. From linear regression analysis, EUR in Indonesia in 2022 is influenced by TPT, JPUP, and Investment. In addition to these variables, the PG variable also affects EUR in Aceh, North Sumatra, West Sumatra, and Riau Provinces according to GWR analysis with a fixed gaussian weighting function

Keywords


Educated Unemployment Rate, Fixed Gaussian, Geographically Weighted Regression, Linear Regression

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DOI: http://dx.doi.org/10.24014/icopss.v4i1.35855

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