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All Journal Infotech: Journal of Technology Information
Imanuel, Marchelle
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Computer Science & IT

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STUDI PERBANDINGAN KEAKURATAN MODEL GLM DAN SVM DALAM MEMPREDIKSI TINGKAT PENGANGGURAN DI INDONESIA Nindito, Hendro; Imanuel, Marchelle; Calvin, Calvin; Lukman, Michelle Pandojo
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.374

Abstract

Unemployment is one of the major issues faced by Indonesia. As of February 2024, the open unemployment rate in Indonesia reached 4.82% of the total labor force. The decline in labor force participation rates and the Human Development Index (HDI) in provinces with high open unemployment rates indicates a correlation as a key contributing factor to unemployment.  This study aims to predict the open unemployment rate in regions of Indonesia using the Generalized Linear Model and Support Vector Machine algorithms through Oracle Machine Learning, and to compare the accuracy of both models in predicting regional unemployment levels in Indonesia. The CRISP-DM framework was applied to support a structured analytical process.  The results of the study show that the Generalized Linear Model developed to predict the open unemployment rate in Indonesia achieved a Mean Absolute Error (MAE) of 0.156 and a Root Mean Square Error (RMSE) of 0.246. In comparison, the Support Vector Machine model yielded a lower MAE of 0.014 and an RMSE of 0.097.
Co-Authors Calvin Calvin Lukman, Michelle Pandojo Nindito, Hendro