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ANALISIS DAN PREDIKSI NILAI IMPOR INDONESIA MENGGUNAKAN PENDEKATAN ALGORITMA MACHINE LEARNING

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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

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Detail Information

Series Title

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Call Number

T1840712025

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 58 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.13

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pemograman
Prodi Sistem Komputer, Fakultas Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

NO

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