Skripsi

PREDIKSI INFLASI HARGA PANGAN MENGGUNAKAN PENDEKATAN MACHINE LEARNING BERBASIS MULTIVARIATE TIME SERIES

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Penilaian

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Food price inflation is an important economic indicator that can affect the social and economic stability of a country. This study aims to develop a model for predicting food price inflation in Indonesia using a machine learning approach based on multivariate time series. The data used includes monthly inflation variables as well as market price index data such as opening (open), highest (high), lowest (low), and closing (close) prices, obtained from World Bank. Three machine learning models are utilized in this study: Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). The evaluation is conducted using the Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) metric on both training/validation and testing phases. The results indicate that the SVR model provides the best performance and consistency compared to LSTM and BiLSTM model. These findings demonstrate that SVR is more effective in handling moderate data complexity with limited data compared to the other models used in this study.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005486	T183162	T1831622025	Central Library (Referensi)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1831622025

Publisher

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

Collation

xvi, 125 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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