Prediksi Harga Komoditi Emas Menggunakan Metode Long Short-Term Memory Dengan Penambahan Optimalisasi

Wahyutama Fitri Hidayat, Kartika Handayani, Yesni Malau, Rachmat Adi Purnama, Ahmad Setiadi

Sari


Seiring dengan perkembangan teknologi, peluang muncul di setiap aspek kehidupan. Sektor ekonomi merupakan salah satu bidang yang terkena dampak kemajuan teknologi. Salah satu aspek ekonomi yang dipengaruhi secara positif oleh teknologi adalah komoditas emas. Di era saat ini, investor terlibat dalam perdagangan emas harian. Dengan memanfaatkan teknologi, pembelian atau penjualan komoditas emas dapat dihitung dengan cermat. Berdasarkan hal tersebut, model pembelajaran mesin dirancang menggunakan LSTM dan eksperimen dilakukan dengan penambahan pengoptimalan ADAM, NADAM, dan ADAMAX untuk menemukan nilai terbaik. Eksperimen mengungkapkan bahwa optimasi terbaik dicapai dengan menggunakan pemisahan data untuk pelatihan dan pengujian 80:20 dengan optimasi NADAM. Hasil penelitian menggunakan model LSTM dengan optimasi NADAM pada data pelatihan menghasilkan nilai RMSE 0,0199, nilai MSE 0,0003, dan Skor R2 0,9804. Sementara itu, dengan menggunakan data pengujian menghasilkan nilai RMSE sebesar 0,0260, nilai MSE sebesar 0,0003.

Teks Lengkap:

PDF

Referensi


Bastian Sianturi, T., Cholissodin, I., & Yudistira, N. (2023). Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(3), 1101–1107. http://j-ptiik.ub.ac.id

Chen, N. (2024). Exploring the development and application of LSTM variants. Applied and Computational Engineering, 53(1), 103–107. https://doi.org/10.54254/2755-2721/53/20241288

Dasari Siva Sankar, H. S. (2013). Gold Prices Prediction Using Random Forest. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 11(V), 1–23.

Dr. S. Sasikala, D. R. B. (2024). An Enhanced Study on Gold Price Prognosis using Machine Learning. International Journal of Advanced Research in Science, Communication and Technology, 2024, p. 1-7. https://doi.org/https://doi.org/10.48175/ijarsct-18401

El-Rashidy, M. A. (2021). A novel system for fast and accurate decisions of gold-stock markets in the short-term prediction. Neural Comput & Applic. https://doi.org/https://doi.org/10.1007/s00521-020-05019-x

Ferdinandus, Y. R. M., Kusrini, K., & Hidayat, T. (2023). Gold Price Prediction Using the ARIMA and LSTM Models. Sinkron, 8(3), 1255–1264. https://doi.org/10.33395/sinkron.v8i3.12461

Hidayat, W. F., Julianto, M. F., Malau, Y., Setiadi, A., & Sriyadi. (2023). Implementation of LSTM and Adam Optimization as a Cryptocurrency Polygon Price Predictor. 2023 International Conference on Information Technology Research and Innovation, ICITRI 2023, 123–127. https://doi.org/10.1109/ICITRI59340.2023.10249571

Jevtić, A., Riznić, D., & Tomić, M. (2024). Gold price prediction based on the Monte Carlo method. XX(May), 201–209. https://doi.org/10.5937/imcsm24020j

K, D. S., B, S., Y, R., Shreya, A., & Kavitha, D. (2024). Data Mining Strategies for Gold Price Prediction Using Multi-Factorial Influences. International Journal For Multidisciplinary Research, 6(3), 1–15. https://doi.org/10.36948/ijfmr.2024.v06i03.18567

Mahajan, R., Patil, P., Chikmurge, D., & Barve, S. (2023). Forecasting Gold Price using Ensemble based Machine Learning Approach. 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–6. https://doi.org/10.1109/ICSES60034.2023.10465360

Manjula, K. A., & Karthikeyan, P. (2019). Gold price prediction using ensemble based machine learning techniques. Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019, 2019-April(Icoei), 1360–1364.

Mustapha, A., Mohamed, L., & Ali, K. (2021). Comparative study of optimization techniques in deep learning: Application in the ophthalmology field. Journal of Physics: Conference Series, 1743(1). https://doi.org/10.1088/1742-6596/1743/1/012002

Nugroho, N. C. T., & Hidayat, E. Y. (2024). Implementation of Adam Optimizer using Recurrent Neural Network (RNN) Architecture for Diabetes Classification. Jurnal Media Informatika Budidarma, 8(1), 421–429. https://doi.org/10.30865/mib.v8i1.7254

Obayya, M., Maashi, M. S., Nemri, N., Mohsen, H., Motwakel, A., Osman, A. E., Alneil, A. A., & Alsaid, M. I. (2023). Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis. Cancers, 15(3). https://doi.org/10.3390/cancers15030885

Salis, V. E., Kumari, A., & Singh, A. (2019). Prediction of Gold Stock Market Using Hybrid Approach. Emerging Research in Electronics, Computer Science and Technology. https://doi.org/10.1007/978-981-13-5802-9_70

Shanmugavadivu, P., Mary Shanthi Rani M, Chitra P, Lakshmanan S, Nagaraja P, & Vignesh U. (2022). Bio-Optimization of Deep Learning Network Architectures. Security and Communication Networks, 2022(ii). https://doi.org/10.1155/2022/3718340

Singh, N. (2024). Artificial Intelligence-Driven Model for Gold Price Prediction. Interantional Journal of Scientific Research in Engineering and Management, 08(05), 1–5. https://doi.org/10.55041/ijsrem33199

Tripurana, N., Kar, B., Chakravarty, S., Paikaray, B. K., & Satpathy, S. (2021). Gold Price Prediction Using Machine Learning Techniques. CEUR Workshop Proceedings, 3283, 274–281. https://doi.org/10.54097/gdm0kc53

Trivedi, U. B., Somvanshi, T. V. S., & J, S. P. (2022). Gold prices prediction: Comparative study of multiple forecasting models. YMER Digital, 21(07), 745–764. https://doi.org/10.37896/ymer21.07/60

Wang, H., Dong, X., Qu, H., Liao, J., & Ma, D. (2024). Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series. Advances in Economics, Management and Political Sciences, 85(1), 118–124. https://doi.org/10.54254/2754-1169/85/20240857

Yi, D., Ahn, J., & Ji, S. (2020). An effective optimization method for machine learning based on ADAM. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031073




DOI: https://doi.org/10.31294/infortech.v6i2.24440

DOI (PDF): https://doi.org/10.31294/infortech.v6i2.24440.g6676

Refbacks

  • Saat ini tidak ada refbacks.


Dipublikasikan oleh LPPM Universitas Bina Sarana Informatika

Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License