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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Iman Priyadi
Magister Ilmu Komputer, Universitas Putra Indonesia YPTK Padang
Computer Science & IT

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Data Mining Predictive Modeling for Prediction of Gold Prices Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices Iman Priyadi; Julius Santony; Jufriadif Na'am
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 2 (2019): September 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i2.6864

Abstract

Gold is an investment instrument that is quite safe from inflationary attacks, and gold is one aspect of initiating investment. Can by buying gold in physical form and then selling when the price has risen high or by digitally investing gold. One of them is by trading gold online. To maximize the benefits of gold trading, a gold price prediction (XAUUSD) is needed for traders. This study aims to (1) Analyze various factors that influence the price of gold (2) Provide recommendations about the prediction of gold prices. Materials that will be used as objects of research to produce gold price predictions include historical XAUUSD (Gold) data itself, historical crude oil data, historical dollar data (USD IDR) and BI 7-Day Repo Rate (BI Rate). ), in producing the prediction of the gold price used Mining Predictive Modeling data using the linear regression function. The results to be achieved from this study is to provide accurate gold price predictions so that it can be used as a reference in making decisions to buy / sell positions in trading. The prediction of the XAUUSD gold price generated is expected to provide significant interest to the investment players (traders) in order to maximize the profit generated.From the results of the trading tests that have been carried out, the implementation of predictive modeling data mining using a linear regression function produces recommendations for gold price predictions (XAUUSD) with an accuracy of 85%.
Co-Authors Jufriadif Na`am, Jufriadif Julius Santony