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All Journal Jurnal Mahasiswa Fakultas Hukum Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Kewarganegaraan Jurnal Pendidikan Terapan Causa: Jurnal Hukum dan Kewarganegaraan Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah
Adam Ramadhan
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Peramalan Harga Emas Berbasis Time Series Menggunakan Arsitektur LSTM Deep Learning Diva Arifal Adha; Adam Ramadhan; Habil Maulana; Patlan Putra Humala Harahap; Edi Ismanto
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9980

Abstract

Gold is one of the most influential commodities in the global economy. Its high price volatility poses a significant challenge for investors, financial analysts, and policymakers in formulating effective strategies and making accurate decisions. Therefore, an accurate prediction method is needed to forecast future gold price movements. This study aims to forecast gold prices using a deep learning approach with the Long Short-Term Memory (LSTM) algorithm. The LSTM model is capable of learning long-term dependencies in time-series data, making it highly suitable for modeling complex and dynamic financial data. The data used in this study consists of daily historical gold prices obtained from reliable sources. A preprocessing phase was carried out to clean and normalize the data before training the model. Furthermore, this study compares the performance of the LSTM model with the Multilayer Perceptron (MLP) model to examine differences in prediction accuracy. Evaluation metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess model performance. The results show that the LSTM model provides more accurate predictions compared to MLP, with lower error values and better model stability. In conclusion, the deep learning approach, particularly the LSTM model, can serve as an effective alternative for gold price forecasting and support data-driven decision-making in the financial sector.
Co-Authors Akmar Akilah Kautsar Al Maududi Bahmar Argya Attallah Apryano Bekti Setiadi Diva Arifal Adha Edi Ismanto Fransisco Frisco Fernando Habil Maulana Hajar Dewantara Ilma Wulansari Hasdiansa Okma Yendri Patlan Putra Humala Harahap Rhena J Rizqy Pratama Erdiyanto Silvy Sondari Gadzali Wahyu Hidayat WAHYUDI