Issue

Vol. 10, No. 1, February 2025

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Analyzing Perceptron Algorithm for Global Gold Price Prediction using Quantum Computing Approach

https://doi.org/10.22219/kinetik.v10i1.2024
Solikhun
STIKOM Tunas Bangsa
https://orcid.org/0009-0003-1182-9914
Muhammad Rahmansyah Siregar
STIKOM Tunas Bangsa

Corresponding Author(s) : Solikhun

solikhun@amiktunasbangsa.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 1, February 2025

Abstract

The price of gold has garnered significant attention in the world of finance and investment due to its role as a safe haven asset and an indicator of global economic stability. An inherent risk of investing in gold is the daily fluctuation in prices, which can rise, fall, or remain stable. Investors are constantly seeking accurate ways to predict gold price movements in order to make informed investment decisions. While classic algorithms like artificial neural networks have been used for gold price prediction, they often struggle with analyzing complex data and identifying the hidden patterns within large datasets. It is widely acknowledged that accurately and consistently predicting the gold price movements, exchange rate, and whether the gold price will rise or fall is very challenging. To address this challenge, this study explored the use of quantum perceptron algorithm for predicting global gold prices. This approach harnesses the principles of quantum computing to improve the efficiency and performance of neural network models. Quantum computers can perform multiple computations simultaneously, enabling the solution of problems that are difficult for classical computers. This study utilized global gold data from January 2018 to December 2022, with 80:20 split of training and testing data; data from January 2018 to December 2021 for training and data from January 2022 to December 2022 for testing. This study aims to offer insights into the potential and application of quantum algorithms in predicting gold prices. The research involved an analysis of global gold price predictions using the quantum perceptron algorithm and quantum computing.

Keywords

Artificial Neural Network Quantum Computing Global Gold Price Perceptron Artificial Intelligence
Solikhun, & Siregar, M. R. . (2025). Analyzing Perceptron Algorithm for Global Gold Price Prediction using Quantum Computing Approach. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(1). https://doi.org/10.22219/kinetik.v10i1.2024
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. S. Darmawan and M. S. Saiful Haq, “Analisis Pengaruh Makroekonomi, Indeks Saham Global, Harga Emas Dunia Dan Harga Minyak Dunia Terhadap Indeks Harga Saham Gabungan (IHSG),” Jurnal Riset Ekonomi dan Bisnis, vol. 15, no. 2, p. 95, Aug. 2022. http://dx.doi.org/10.26623/jreb.v15i2.4381
  2. R. R. A. Siregar, T. Djatna, S. S. M. P. Sarmose Manggara Putra, and I. Saputra, “Double Exponential Smoothing Berimputasi LOCF Dan Linear Interpolation Dalam Akurasi Peramalan Harga Harian Emas,” Kilat, vol. 10, no. 1, pp. 208–222, 2021. https://doi.org/10.33322/kilat.v10i2.1200
  3. M. Guntur, J. Santony, and Y. Yuhandri, “Prediksi Harga Emas Dengan Menggunakan Metode Naïve Bayes Dalam Investasi Untuk Meminimalisasi Resiko,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 2, no. 1, pp. 354–360, Apr. 2018. https://doi.org/10.29207/resti.v2i1.276
  4. M. Al Haris, “Peramalan Harga Emas Dengan Model Generalized Autoregressive Conditional Heteroscedasticity (GARCH),” Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam, vol. 3, no. 1, p. 19, Jul. 2020. https://doi.org/10.32493/jsmu.v3i1.5263
  5. S. Wahyuningsih, Kusrini, and Hanafi, “Literature Study On Predicting Gold Prices Using Machine Learning,” DIELEKTRIKA, vol. 10, no. 2, pp. 112–117, Aug. 2023. https://doi.org/10.29303/dielektrika.v10i2.335
  6. S. Ben Jabeur, S. Mefteh-Wali, and J. L. Viviani, “Forecasting Gold Price With The XGBOOST Algorithm And SHAP Interaction Values,” Annals of Operations Research, vol. 334, no. 1–3, pp. 679–699, 2024. https://doi.org/10.1007/s10479-021-04187-w
  7. Z. Alameer, M. A. Elaziz, A. A. Ewees, H. Ye, and Z. Jianhua, “Forecasting Gold Price Fluctuations Using Improved Multilayer Perceptron Neural Network And Whale Optimization Algorithm,” Resources Policy, vol. 61, no. September 2018, pp. 250–260, 2019. https://doi.org/10.1016/j.resourpol.2019.02.014
  8. M. Yanto, S. Sanjaya, Y. Yulasmi, D. Guswandi, and S. Arlis, “Implementation Multiple Linear Regresion In Neural Network Predict Gold Price,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 3, p. 1635, Jun. 2021. http://doi.org/10.11591/ijeecs.v22.i3.pp1635-1642
  9. D. Nababan and E. Alexander, “Implementasi Metode Fuzzy Time Series Dengan Model Algoritma Chen Untuk Memprediksi Harga Emas,” Jurnal Teknik Informatika, vol. 13, no. 1, pp. 71–78, 2020. https://doi.org/10.15408/jti.v13i1.15516
  10. N. Liu and P. Rebentrost, “Quantum Machine Learning For Quantum Anomaly Detection,” Physical Review A, vol. 97, no. 4, p. 042315, Apr. 2018. https://doi.org/10.1103/PhysRevA.97.042315
  11. A. Jadhav, A. Rasool, and M. Gyanchandani, “Quantum Machine Learning: Scope For Real-World Problems,” Procedia Computer Science, vol. 218, pp. 2612–2625, 2023. https://doi.org/10.1016/j.procs.2023.01.235
  12. P. Kairon and S. Bhattacharyya, “Comparative Study Of Variational Quantum Circuit And Quantum Backpropagation Multilayer Perceptron For COVID-19 Outbreak Predictions,” arXiv, pp. 1–10, 2020, doi: https://doi.org/10.48550/arXiv.2008.07617.
  13. M. Schuld and N. Killoran, “Is Quantum Advantage the Right Goal for Quantum Machine Learning?,” PRX Quantum, vol. 3, no. 3, p. 030101, Jul. 2022. https://doi.org/10.1103/PRXQuantum.3.030101
  14. S. Mangini, F. Tacchino, D. Gerace, D. Bajoni, and C. Macchiavello, “Quantum Computing Models For Artificial Neural Networks,” Europhysics Letters, vol. 134, no. 1, p. 10002, Apr. 2021. https://dx.doi.org/10.1209/0295-5075/134/10002
  15. K. Beer, M. Khosla, J. Köhler, T. J. Osborne, and T. Zhao, “Quantum Machine Learning Of Graph-Structured Data,” Physical Review A, vol. 108, no. 1, p. 012410, Jul. 2023. https://doi.org/10.1103/PhysRevA.108.012410
  16. Rabiatul Adawiyah and Munifah, “Eksplorasi Kapasitas Pengkodean Amplitudo Untuk Model Quantum Machine Learning,” Informatika: Jurnal Teknik Informatika dan Multimedia, vol. 3, no. 1, pp. 38–58, May 2023. https://doi.org/10.51903/informatika.v3i1.232
  17. N. Mishra et al., “Quantum Machine Learning: A Review and Current Status,” in Springer Nature Singapore Pte Ltd, 2021, pp. 101–145. http://dx.doi.org/10.13140/RG.2.2.22824.72964
  18. J. Carrasquilla and G. Torlai, “How To Use Neural Networks To Investigate Quantum Many-Body Physics,” PRX Quantum, vol. 2, no. 4, p. 040201, Nov. 2021. https://doi.org/10.1103/PRXQuantum.2.040201
  19. A. Daskin, “A Simple Quantum Neural Net with a Periodic Activation Function,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Oct. 2018, pp. 2887–2891. https://doi.org/10.1109/SMC.2018.00491
  20. Z. Abohashima, M. Elhosen, E. H. Houssein, and W. M. Mohamed, “Classification with Quantum Machine Learning: A Survey,” arXiv, no. 9, pp. 1–16, 2020. https://doi.org/10.48550/arXiv.2006.12270
  21. V. Kulkarni, M. Kulkarni, and A. Pant, “Quantum Computing Methods For Supervised Learning,” Quantum Machine Intelligence, vol. 3, no. 2, p. 23, Dec. 2021. https://doi.org/10.1007/s42484-021-00050-0
  22. R. Zhao and S. Wang, “A Review Of Quantum Neural Networks: Methods, Models, Dilemma,” arXiv, no. 299, pp. 1–14, 2021, doi: https://doi.org/10.48550/arXiv.2109.01840.
  23. X. Gao, Z.-Y. Zhang, and L.-M. Duan, “A Quantum Machine Learning Algorithm Based On Generative Models,” Science Advances, vol. 4, no. 12, pp. 1–7, Dec. 2018. https://doi.org/10.1126/sciadv.aat9004
  24. M. R. Pulicharla, “Hybrid Quantum-Classical Machine Learning Models: Powering the Future of AI,” Journal of Science & Technology, vol. 4, no. 1, pp. 40–65, 2023. https://doi.org/10.55662/JST.2023.4102
  25. P. Huber, J. Haber, P. Barthel, J. J. García-Ripoll, E. Torrontegui, and C. Wunderlich, “Realization Of A Quantum Perceptron Gate With Trapped Ions,” arXiv, pp. 1–5, 2021, doi: https://doi.org/10.48550/arXiv.2111.08977.
  26. R. Wiersema and H. J. Kappen, “Implementing Perceptron Models With Qubits,” Physical Review A, vol. 100, no. 2, p. 020301, Aug. 2019. https://doi.org/10.1103/PhysRevA.100.020301
  27. A. Macaluso, F. Orazi, M. Klusch, S. Lodi, and C. Sartori, “A Variational Algorithm for Quantum Single Layer Perceptron,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2023, pp. 341–356. https://doi.org/10.1007/978-3-031-25891-6_26
  28. Hafid Akbar Fikri, “Prediksi Harga Emas Dengan Algoritma Backpropagation,” Jurnal Sains Komputer & Informatika (J-SAKTI), vol. 7, no. 1, pp. 182–189, 2023. http://dx.doi.org/10.30645/j-sakti.v7i1.582
  29. A. Prasetya Wibawa, W. Lestar, A. Bella Putra Utama, I. Tri Saputra, and Z. Nabila Izdihar, “Multilayer Perceptron untuk Prediksi Sessions pada Sebuah Website Journal Elektronik,” Indonesian Journal of Data and Science, vol. 1, no. 3, pp. 57–67, Dec. 2020. https://doi.org/10.33096/ijodas.v1i3.15
  30. S. Khotijah, L. Sarifah, and A. Fuaddiyah, “Prediksi Harga Emas Menggunakan Metode Radial Basis Function Neural Network (RBFNN),” Jurnal Sains Matematika dan Statistika, vol. 10, no. 1, p. 20, Feb. 2024. http://dx.doi.org/10.24014/jsms.v10i1.20890
  31. S. Solikhun and V. Yasin, “Analisis Quantum Perceptron Untuk Memprediksi Jumlah Pengunjung Ucok Kopi Pematangsiantar Pada Masa Pandemi Covid-19,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 8, no. 1, p. 162, 2022. https://dx.doi.org/10.26418/jp.v8i1.52191
  32. M. Guntur, J. Santony, and Y. Yuhandri, “Prediksi Harga Emas Dengan Menggunakan Metode Naïve Bayes Dalam Investasi Untuk Meminimalisasi Resiko,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 2, no. 1, pp. 354–360, 2018. https://doi.org/10.29207/resti.v2i1.276
  33. A. Abbas, D. Sutter, C. Zoufal, A. Lucchi, A. Figalli, and S. Woerner, “The Power Of Quantum Neural Networks,” Nature Computational Science, vol. 1, no. 6, pp. 403–409, Jun. 2021. https://doi.org/10.1038/s43588-021-00084-1
  34. A. chandra Saputra, “Penentuan Parameter Learning Rate Selama Pembelajaran Jaringan Syaraf Tiruan Backpropagation Menggunakan Algoritma Genetika,” Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 14, no. 2, pp. 202–212, Aug. 2020. https://doi.org/10.47111/jti.v14i2.1141
  35. P. R. Iswardani, M. Sudarma, and L. Jasa, “Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network,” Majalah Ilmiah Teknologi Elektro, vol. 20, no. 1, p. 153, Apr. 2021. https://doi.org/10.24843/MITE.2021.v20i01.P18
  36. N. Kahar and W. Aritonang, “Implementasi Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Dalam Penentuan Program Studi Mahasiswa Baru,” JURNAL AKADEMIKA, vol. 14, no. 2, pp. 74–80, Apr. 2022. http://dx.doi.org/10.53564/akademika.v14i2.864
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 0 Download : 0

Downloads

Download data is not yet available.