Gold Price Prediction Using Long-Short Term Memory Algorithm Based on Web Application

Rodiatul Adawiyah Dalimunthe, Rizal Tjut Adek, Cut Agusniar

Abstract


Gold is a significant investment asset, particularly in times of economic instability. Various factors, including decisions by financial authorities, inflation, and global economic dynamics, influence the fluctuations in gold prices. Accurately predicting gold prices is valuable for investors when making investment decisions. This study aims to utilize the Long Short-Term Memory (LSTM) algorithm for predicting gold prices and develop a web-based application connected to Yahoo Finance to acquire real-time gold price data. The LSTM algorithm was chosen because it handles time series data with long-term dependencies. LSTM has an architecture that allows the model to retain relevant information over long periods and forget irrelevant data. In this study, the developed LSTM model produced a Mean Absolute Error (MAE) of 19.81, indicating that the average prediction deviates by approximately 19.81 units from the actual value. Furthermore, an average Mean Absolute Percentage Error (MAPE) of 0.83% demonstrates the high prediction accuracy. The results of this study show that LSTM is an effective method for predicting gold prices. The resulting web application allows users to access gold price projections interactively, thereby assisting investors in making more accurate and data-driven decisions with easy access. Additionally, the web application offers customizable features such as adjusting prediction parameters and visualizing results in real time.  These features not only enhance user engagement but also improve decision-making processes. This research provides a practical tool for optimizing investment strategies in a dynamic economic environment by leveraging machine learning and seamless web integration.


Keywords


Gold, Long-Short Term Memory Prediction, Website, Yahoo Finance

Full Text:

PDF

References


G. Budiprasetyo, M. Hani’ah, and D. Z. Aflah, “Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM),” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 8, no. 3, pp. 164–172, Jan. 2023, doi: 10.25077/teknosi.v8i3.2022.164-172.

R. S. Sinambela, M. Ula, and A. F. Ulva, "Prediksi Harga Emas Menggunakan Algoritma Regresi Linear Berganda Dan Support Vector Machine (SVM)," Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 12, no. 2, p. 253, Apr. 2024, doi: 10.26418/justin.v12i2.73386.

P. Bidang, K. Sains, P. Informatika, S. Putro, A. Hermawan, and D. Avianto, “Prediksi Harga Emas Menggunakan Algoritma Long Short-Term Memory (LSTM) dan Linear Regression,” Jurnal Edik Informatika, vol. 9, no. 2, 2023, doi: 10.22202/ei.2023.v9i2.6990.

A. Tholib, N. K. Agusmawati, and F. Khoiriyah, “Prediksi Harga Emas Menggunakan Metode LSTM dan GRU,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 3, Aug. 2023, doi: 10.23960/jitet.v11i3.3250.

C. Rahayu, D. Abdullah, and Z. Yunizar, "Implementation Of Long Short Term Memory (LSTM) Algorithm For Predicting Stock Price Movements Of LQ45 Index (Case Study : BBCA Stock Price)," 2023. [Online]. Available: https://bestijournal.org

A. Sujjada and F. Sembiring, “Prediksi Harga Bitcoin Menggunakan Algoritma Long ShortTerm Memory,” Jurnal Invotek Polbeng Seri informatika, vol. 9, no. 1, 2024, doi: :DOI10.35314/isi.v9i1.4247.

L. Sahrina Hasibuan and Y. Novialdi, "Prediction of Bulk and Packaged Cooking Oil Prices Using the Long Short-Term Memory (LSTM) Algorithm," Jurnal Komputer Agri-Informatika, pp. 149–157, 2023, doi: http://repository.ipb.ac.id/handle/123456789/116042.

M. David, I. Cholissodin, and N. Yudistira, “Prediksi Harga Cabai menggunakan Metode Long-Short Term Memory (Case Study : Kota Malang),” Jurnal Pengembangan teknologi Informasi dan Ilmu Komputer, vol. 7, no. 3, pp. 1214–1219, 2023, doi: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12406.

F. Husaini, I. Permana, M. Afdal, and F. N. Salisah, "Penerapan Algoritma Long Short-Term Memory untuk Prediksi Produksi Kelapa Sawit," MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 366–374, Feb. 2024, doi: 10.57152/malcom.v4i2.1187.

R. Julian and M. R. Pribadi, “Peramalan Harga Saham Pertambangan Pada Bursa Efek Indonesia (BEI) Menggunakan Long Short Term Memory (LSTM),” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, 2021, doi: https://doi.org/10.35957/jatisi.v8i3.1159.

U. Khaira et al., “Prediksi Kemunculan Titik Panas Di Lahan Gambut Provinsi Riau Menggunakan Long Short Term Memory,” Jurnal Informatika : Jurnal Pengembangan IT (JPIT), vol. 5, no. 3, 2020, doi: https://doi.org/10.30591/jpit.v5i3.1931.

B. A. Aprian, Y. Azhar, V. Rahmayanti, and S. Nastiti, “Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory,” Jurnal Komputer Terapan, vol. 6, no. 2, 2020, doi: https://doi.org/10.35143/jkt.v6i2.3621.

A. R. H. Dwika and D. Avianto, “Implementasi Algoritma LSTM untuk Prediksi Harga Cabai Merah Keriting di Yogyakarta,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 5, no. 1, pp. 635–648, Jan. 2024, doi: 10.35870/jimik.v5i1.534.

F. Nur Iman and D. Wulandari, “Prediksi Harga Saham Menggunakan Metode Long Short Term Memory,” Jurnal Ilmu Komputer dan Pendidikan, vol. 1, pp. 601–616, 2023, [Online]. Available: https://journal.mediapublikasi.id/index.php/logic

F. Indra Sanjaya and D. Heksaputra, “Prediksi Rerata Harga Beras Tingkat Grosir Indonesia dengan Long Short Term Memory,” vol. 7, no. 2, pp. 163–174, 2020, [Online]. Available: http://jurnal.mdp.ac.id

S. R. Siregar and R. Widyasari, “PERAMALAN HARGA CRUDE OIL MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) DALAM RECURRENT NEURAL NETWORK (RNN),” Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 4, no. 3, 2023, doi: 10.46306/lb.v4i3.

M. Ikhsan, M. Safriani, C. S. Silvia, and R. Dari, "Prediction of Land Erosion Events in the Down Stream Kreung Meureubo Watershed West Aceh District," International Journal of Engineering, Science and Information Technology, vol. 1, no. 4, pp. 70–76, Nov. 2021, doi: 10.52088/ijesty.v1i4.173.

V. Riandaru Prasetyo, S. Axel, J. Timothy Soebroto, D. Sugiarto, S. Ardi Winatan, and S. Daniel Njudang, “Prediksi Harga Emas Berdasarkan Data gold.org menggunakan Metode Long Short Term Memory Gold Price Prediction Based on Gold.org Data using the Long Short Term Memory Method,” SISTEMASI: Jurnal Sistem Informasi , pp. 623–629, 2022, doi: https://doi.org/10.32520/stmsi.v11i3.1999.

T. G. Lasijan, R. Santoso, and A. R. Hakim, “Prediksi Harga Emas Dunia Menggunakan Metode Long Short-Term Memory,” Jurnal Gaussian, vol. 12, no. 2, pp. 287–295, Jul. 2023, doi: 10.14710/j.gauss.12.2.287-295.

A. Arwansyah, S. Suryani, H. SY, U. Usman, A. Ahyuna, and S. Alam, "Time Series Forecasting Menggunakan Deep Gated Recurrent Units," Digital Transformation Technology, vol. 4, no. 1, pp. 410–416, Jun. 2022, doi: 10.47709/digitech.v4i1.4141.

J. Khatib Sulaiman, T. Haryanto, and I. Pertanian Bogor, “Pemanfaatan Model Long Short Term Memory (LSTM) Untuk Prediksi Harga Emas Sebagai Instrumen Investasi Dalam Mempersiapkan Ancaman Resesi Global 2023,” Indonesian Journal of Computer Science Attribution, vol. 12, no. 2, p. 614, 2023, doi: https://doi.org/10.33022/ijcs.v12i2.3176.

Sabar Sautomo and Hilman Ferdinandus Pardede, “Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 99–106, Feb. 2021, doi: 10.29207/resti.v5i1.2815.




DOI: https://doi.org/10.52088/ijesty.v5i1.724

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Rodiatul Adawiyah Dalimunthe, Rizal Tjut Adek, Cut Agusniar

International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674