IMPLEMENTASI MODEL LSTM, GRU, BILSTM, DAN BIGRU DALAM PREDIKSI HARGA NIKEL

Authors

  • Muhammad Atharsyah Universitas Teknologi Yogyakarta
  • Moh. Ali Romli

DOI:

https://doi.org/10.36595/jire.v7i2.1317

Keywords:

LSTM, GRU, BiLSTM, BiGRU, Nikel

Abstract

Penggunaan nikel di dunia kini semakin meluas. Nikel merupakan bahan utama pembuatan baja tahan karat dan baterai mobil listrik. Seiring dengan kemajuan kendaraan listrik yang menggunakan baterai nikel, harga nikel pun menjadi topik menarik, terutama bagi investor yang berkeinginan untuk berinvestasi di industri nikel. Studi terkini tentang prediksi harga nikel memanfaatkan model Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), dan Bidirectional Gated Recurrent Unit (BiGRU), dengan data historis harga nikel dalam USD. Pembangunan model prediksi terbaik melibatkan penyesuaian parameter seperti epoch, learning rate, batch, optimizer, dan penerapan teknik dropout untuk menghindari overfitting. Hasil pengujian menunjukkan bahwa model BiLSTM adalah yang terbaik, dengan R2 Score sebesar 0.86962 dan RMSE sebesar 0.024735, menandakan bahwa model BiGRU memberikan prediksi yang akurat berdasarkan kriteria R2 dan RMSE.

References

F. Menz, M. Bauer, O. Böse, M. Pausch, And M. A. Danzer, “Investigating The Thermal Runaway Behaviour Of Fresh And Aged Large Prismatic Lithium-Ion Cells In Overtemperature Experiments,” Batteries, Vol. 9, No. 3, Mar. 2023, Doi: 10.3390/Batteries9030159.

X. Zhou Et Al., “Risk Transmission Of Trade Price Fluctuations From A Nickel Chain Perspective: Based On Systematic Risk Entropy And Granger Causality Networks,” Entropy, Vol. 24, No. 9, Sep. 2022, Doi: 10.3390/E24091221.

B. Lim, H. S. Kim, And J. Park, “Implicit Interpretation Of Indonesian Export Bans On Lme Nickel Prices: Evidence From The Announcement Effect,” Risks, Vol. 9, No. 5, May 2021, Doi: 10.3390/Risks9050093.

R. S. Ekhlakov And V. A. Sudakov, “Forecasting The Cost Of Quotes Using Lstm & Gru Networks,” Keldysh Institute Preprints, No. 17, Pp. 1–13, 2022, Doi: 10.20948/Prepr-2022-17.

A. Muhammad Raihan And J. Ceilendra Saksana, “Analysis Of Stock Price Index Volatility In Indonesia Using Macroeconomic Variables And Global Economic Uncertainty Index,” 2023. Doi: Https://Doi.Org/10.24252/Assets.V13i1.37616.

E. Ahmadzadeh, H. Kim, O. Jeong, N. Kim, And I. Moon, “A Deep Bidirectional Lstm-Gru Network Model For Automated Ciphertext Classification,” Ieee Access, Vol. 10, Pp. 3228–3237, 2022, Doi: 10.1109/Access.2022.3140342.

T. Lees Et Al., “Benchmarking Data-Driven Rainfall-Runoff Models In Great Britain: A Comparison Of Long Short-Term Memory (Lstm)-Based Models With Four Lumped Conceptual Models,” Hydrol Earth Syst Sci, Vol. 25, No. 10, Pp. 5517–5534, Oct. 2021, Doi: 10.5194/Hess-25-5517-2021.

M. Haris, “Analysis Of The Application Of Hyperparameter Tuning In Machine Learning To Increase The Accuracy Of Sales-Level Prediction,” 2024. [Online]. Available: Http://E-Journal.Stmiklombok.Ac.Id/Index.Php/Jireissn.2620-6900

K. H. Suradiradja, “Algoritme Machine Learning Multi-Layer Perceptron Dan Recurrent Neural Network Untuk Prediksi Harga Cabai Merah Besar Di Kota Tangerang,” Faktor Exacta, Vol. 14, No. 4, P. 194, Jan. 2022, Doi: 10.30998/Faktorexacta.V14i4.10376.

Y. Karyadi And H. Santoso, “Prediksi Kualitas Udara Dengan Metoda Lstm, Bidirectional Lstm, Dan Gru,” Jurnal Teknik Informatika Dan Sistem Informasi, Vol. 9, No. 1, Pp. 671–684, 2022.

F. Ferdiawan, B. Hartono, J. T. Lomba, J. No, And S. 50241, “Deteksi Suara Chord Piano Menggunakan Metode Convolutional Neural Network,” 2022. [Online]. Available: Http://E-Journal.Stmiklombok.Ac.Id/Index.Php/Jire

U. I. Arfianti, D. C. R. Novitasari, N. Widodo, Moh. Hafiyusholeh, And W. D. Utami, “Sunspot Number Prediction Using Gated Recurrent Unit (Gru) Algorithm,” Ijccs (Indonesian Journal Of Computing And Cybernetics Systems), Vol. 15, No. 2, P. 141, Apr. 2021, Doi: 10.22146/Ijccs.63676.

I. P. G. A. Sudiatmika, I. M. A. W. Putra, And W. W. Artana, “The Implementation Of Gated Recurrent Unit (Gru) For Gold Price Prediction Using Yahoo Finance Data: A Case Study And Analysis,” Brilliance: Research Of Artificial Intelligence, Vol. 4, No. 1, Pp. 176–184, Jun. 2024, Doi: 10.47709/Brilliance.V4i1.3865.

Z. M. Shaikh And S. Ramadass, “Unveiling Deep Learning Powers: Lstm, Bilstm, Gru, Bigru, Rnn Comparison,” Indonesian Journal Of Electrical Engineering And Computer Science, Vol. 35, No. 1, Pp. 263–273, Jul. 2024, Doi: 10.11591/Ijeecs.V35.I1.Pp263-273.

D. I. Puteri, “Implementasi Long Short Term Memory (Lstm) Dan Bidirectional Long Short Term Memory (Bilstm) Dalam Prediksi Harga Saham Syariah,” Euler?: Jurnal Ilmiah Matematika, Sains Dan Teknologi, Vol. 11, No. 1, Pp. 35–43, May 2023, Doi: 10.34312/Euler.V11i1.19791.

Published

2024-11-01