PEMODELAN PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN ALGORITMA MACHINE LEARNING

ANNASTASYA, TANTI and Passarella, Rossi (2025) PEMODELAN PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN ALGORITMA MACHINE LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Unemployment is a major global issue with significant socioeconomic impacts, including reduced individual well-being, increased inequality, and weakened economic stability. For Indonesia, as the largest economy in Southeast Asia, understanding the determinants of unemployment and developing reliable forecasting methods are crucial for effective policymaking. This study proposes a two-stage hybrid forecasting framework that integrates ARIMA forecasting to generate predictor variables, which are then applied as inputs to machine learning models. Using Indonesia’s long-term historical data from 1970 to 2023, the results show that the Gradient Boosting Regressor achieves the best performance, with a Mean Absolute Percentage Error (MAPE) of 14.7% on the 2023 holdout data. Feature importance analysis reveals that higher education, GDP growth, and foreign direct investment (FDI) are the most influential variables in predicting unemployment rates. Based on this framework, Indonesia’s unemployment rate is projected to undergo moderate fluctuations during 2024–2028.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Tanti Annastasya
Date Deposited:	12 Sep 2025 06:48
Last Modified:	12 Sep 2025 06:48
URI:	http://repository.unsri.ac.id/id/eprint/183881

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