ANALISIS DAN PREDIKSI NILAI IMPOR INDONESIA MENGGUNAKAN PENDEKATAN ALGORITMA MACHINE LEARNING

GUSLEO, FERDINAND and Passarella, Rossi (2025) ANALISIS DAN PREDIKSI NILAI IMPOR INDONESIA MENGGUNAKAN PENDEKATAN ALGORITMA MACHINE LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Accurate forecasting of import values is crucial for effective economic planning and policy-making in emerging economies like Indonesia. This study evaluates the performance of three prominent Machine Learning (ML) models—Support Vector Regression (SVR), Random Forest, and Decision Tree—for forecasting Indonesian goods and services imports. Utilising historical macroeconomic time series data for Indonesia spanning 1970–2023, the models were trained and rigorously evaluated using standard metrics, including mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R2). The results indicate that SVR demonstrated superior performance based on the evaluation metrics, while Decision Tree achieved the highest accuracy in predicting the 2023 import value. The findings suggest that ML models, particularly SVR and Decision Tree, are effective and promising tools for enhancing the precision of Indonesian import forecasting.

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:	Ferdinand Gusleo
Date Deposited:	17 Sep 2025 01:39
Last Modified:	17 Sep 2025 01:39
URI:	http://repository.unsri.ac.id/id/eprint/184071

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