The Comparison of Gold Price Prediction Techniques Using Long Short Term Memory (LSTM) And Fuzzy Time Series (FTS) Method

Putry Shan Alodia Surya Pangestu (Informatics Engineering Program, Faculty of Industrial Technology, University of Trisakti)
Abdul Rochman (Informatics Engineering Program, Faculty of Industrial Technology, University of Trisakti)
Ahmad Zuhdi (Informatics Engineering Program, Faculty of Industrial Technology, University of Trisakti)

Intelmatics
Vol. 3 No. 2 (2023): Juli-Desember

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