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All Journal Intelmatics
Ahmad Zuhdi
Informatics Engineering Program, Faculty of Industrial Technology, University of Trisakti
Computer Science & IT

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Journal : Intelmatics

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The Comparison of Gold Price Prediction Techniques Using Long Short Term Memory (LSTM) And Fuzzy Time Series (FTS) Method Putry Shan Alodia Surya Pangestu; Abdul Rochman; Ahmad Zuhdi
Intelmatics Vol. 3 No. 2 (2023): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v3i2.17325

Abstract

Gold is a precious metal that has economic value and is often used as an investment tool. The demand for gold from day to day is increasing, because many know and think that gold can be used as ownership in the form of investment assets that have low risk. Therefore, it is necessary to predict the gold price to avoid losses. This study aims to predict the gold price using a machine learning architecture including deep learning, namely Long Short Term Memory (LSTM) and Fuzzy Time Series (FTS). Several trial processes were carried out in the training process and predict the LSTM and FTS models to get the best results. The data used in this experiment is real data from the period 15 September 2016 – 15 September 2021. The final results obtained from the LSTM method have an RMSE value of training data of 391.95 RMSE, and a value of test data 412.36 RMSE, and the FTS method has an RMSE value of 10449.115791541652
Co-Authors Abdul Rochman Putry Shan Alodia Surya Pangestu