Prediksi Harga Emas Menggunakan Hybrid Deep Learning dengan Mengintegrasikan LSTM-ANN Network dengan Model GARCH

Ramadhan, Thrisna (2025) Prediksi Harga Emas Menggunakan Hybrid Deep Learning dengan Mengintegrasikan LSTM-ANN Network dengan Model GARCH. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Gold investment is increasingly favored by the public as a relatively stable long- term investment instrument. However due to the unpredictable nature of gold price changes, investors often struggle to make optimal investment decisions. Complex factors such as market volatility, economic news, changes in monetary policy, inflation, and geopolitical uncertainty lead to sharp movements in gold prices, which are difficult to predict using conventional methods. This study aims to develop an accurate gold price prediction model using a hybrid deep learning approach by integrating the LSTM-ANN Network and the GARCH model. This hybrid method combines the strengths of the LSTM- ANN Network in capturing temporal patterns and non-linear trends in historical price data, with the ability of the GARCH model to handle gold price volatility. This approach is expected to provide more holistic and accurate predictions compared to conventional forecasting methods. This study uses historical gold price data from reliable sources as the basis for prediction, focusing on gold price forecasting over a specific time period. The results of this study are expected to contribute to the development of commodity price prediction models, particularly gold, and provide a tool to help investors make more informed investment decisions. The findings of this study may serve as a starting point for more advanced exploration in the use of hybrid deep learning approaches for predicting other commodity prices.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Wahanani, Henni Endah	NIDN0022097811	henniendah@upnjatim.ac.id
Thesis advisor	Aditiawan, Firza Prima	NIDN0023058605	firzaprima.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Thrisna Ramadhan

Date Deposited:

24 Jul 2025 08:12

Last Modified:

24 Jul 2025 08:12

URI:

https://repository.upnjatim.ac.id/id/eprint/40714