Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Brilliance: Research of Artificial Intelligence
Putra , I Made Agus Widiana
Unknown Affiliation
Decision Sciences, Operations Research & Management Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
The Implementation of Gated Recurrent Unit (GRU) for Gold Price Prediction Using Yahoo Finance Data: A Case Study and Analysis Sudiatmika, I Putu Gede Abdi; Putra , I Made Agus Widiana; Artana, Wayan Widya
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3865

Abstract

Gold is a precious metal resistant to corrosion and oxidation, highly valued in investment and trade. Currently, the demand for gold is increasing as it is considered a safe haven. This is evidenced by 48% of respondents out of 2,333 respondents choosing gold as the most preferred investment, based on a survey conducted by Jakpat. However, gold actually has a fluctuating price. The fluctuating price of gold worldwide is influenced by many factors such as economic conditions, inflation rate, supply and demand of gold, and the US dollar exchange rate. Therefore, there is a need for a prediction that can estimate the price of gold based on the movement of gold prices in previous periods. In this study, an evaluation of the performance of GRU for predicting the price of gold will be conducted.. The research methodology includes data collection and processing of gold prices, application of the GRU model, and evaluation of model performance with evaluation metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Gold price data is taken from Yahoo Finance from December 14, 2017, to March 14, 2024, and processed through normalization and data splitting into training and testing sets. The results of the study show that the GRU model is able to predict gold prices with an adequate level of accuracy. Based on the MSE and MAE values, the combination that provides the best performance is a batch size of 64 with 100 epochs, as it yields the lowest MSE and MAE.
The Implementation of Gated Recurrent Unit (GRU) for Gold Price Prediction Using Yahoo Finance Data: A Case Study and Analysis Sudiatmika, I Putu Gede Abdi; Putra , I Made Agus Widiana; Artana, Wayan Widya
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3865

Abstract

Gold is a precious metal resistant to corrosion and oxidation, highly valued in investment and trade. Currently, the demand for gold is increasing as it is considered a safe haven. This is evidenced by 48% of respondents out of 2,333 respondents choosing gold as the most preferred investment, based on a survey conducted by Jakpat. However, gold actually has a fluctuating price. The fluctuating price of gold worldwide is influenced by many factors such as economic conditions, inflation rate, supply and demand of gold, and the US dollar exchange rate. Therefore, there is a need for a prediction that can estimate the price of gold based on the movement of gold prices in previous periods. In this study, an evaluation of the performance of GRU for predicting the price of gold will be conducted.. The research methodology includes data collection and processing of gold prices, application of the GRU model, and evaluation of model performance with evaluation metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Gold price data is taken from Yahoo Finance from December 14, 2017, to March 14, 2024, and processed through normalization and data splitting into training and testing sets. The results of the study show that the GRU model is able to predict gold prices with an adequate level of accuracy. Based on the MSE and MAE values, the combination that provides the best performance is a batch size of 64 with 100 epochs, as it yields the lowest MSE and MAE.
Co-Authors ABDI SUDIATMIKA, I PUTU GEDE Artana, Wayan Widya