Skripsi

PENGEMBANGAN MODEL PREDIKSI NILAI PERTUMBUHAN GDP INDONESIA MENGGUNAKAN ALGORITMA MACHINE LEARNING

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Penilaian

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Accurate prediction of Gross Domestic Product (GDP) growth is essential for guiding effective economic policymaking in Indonesia. This study proposes a hybrid forecasting approach that integrates fuzzy logic and machine learning to improve the accuracy of GDP growth prediction. Using annual macroeconomic data from 1970 to 2023, we developed 19 input features, combining numerical indicators with fuzzy-based representations and a Non-Stationary Fuzzy Time Series (NSFTS) forecast label. Six machine learning models were evaluated, with Random Forest consistently achieving the lowest mean absolute error (MAE) and the highest accuracy in predicting GDP growth for 2023 (99.45%), outperforming all other models. These results demonstrate the superior ability of Random Forest in capturing short-term economic trends. The proposed approach offers practical value for government agencies and policymakers by enabling data-driven economic planning, improving fiscal policy decisions, and supporting early intervention strategies to stabilize growth. This research affirms the potential of hybrid fuzzy–machine learning frameworks as robust tools for macroeconomic forecasting in emerging economies.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005660	T183842	T1838422025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

005.13

Publisher

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

Collation

xiii, 63 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.13

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Sistem Pemograman
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

NO

Other version/related

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