Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

Widi Hastomo, Adhitio Satyo Bayangkari Karno, Nawang Kalbuana, Ervina Nisfiani, Lussiana ETP

Abstract


Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

Keywords


Covid-19; Deep Learning; LSTM; GRU

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References


A. C. Waluyo and M. T. Parasetya, “Pengaruh Manajemen Laba Terhadap Tingkat Oversubscription Pada Umkm Yang Melakukan Initial Public Offering Di Bursa Efek …,” Diponegoro J. …, vol. 10, pp. 1–10, 2021.

A. Fernandez-perez, A. Gilbert, I. Indriawan, and N. H. Nguyen, “COVID-19 pandemic and stock market response : A culture effect,” no. January, 2021.

F. J. Contractor, “The world economy will need even more globalization in the post-pandemic 2021 decade,” J. Int. Bus. Stud., 2021.

B. S. dan A. Fauzi, “IHSG Sempat Anjlok ke Level Paling Rendah,” Suara.com, 2020. [Online]. Available: https://www.suara.com/bisnis/2020/04/24/134110/gegara-corona-bos-bei-ihsg-sempat-anjlok-ke-level-paling-rendah. [Accessed: 18-Jan-2021].

Tri Putra, “5 Saham Blue Chip Ini Tahan Kejatuhan IHSG,” 2020. [Online]. Available: https://www.cnbcindonesia.com/market/20200626153809-17-168348/terima-kasih-5-saham-blue-chip-ini-tahan-kejatuhan-ihsg. [Accessed: 18-Jan-2021].

W. Hastomo and A. Satyo, “Kemampuan Long Short Term Memory Machine,” vol. 4, no. September, pp. 229–236, 2020.

W. Hastomo and A. Satyo, “Long Short Term Memory Machine Learning Untuk Memprediksi Akurasi Nilai Tukar IDR Terhadap USD,” vol. 3, 2019.

E. Islam, M. S., & Hossain, “Foreign exchange currency rate prediction using a GRU-LSTM Hybrid Network,” Soft Comput. Lett., vol. 100009, 2020.

N. Patel, M. M., Tanwar, S., Gupta, R., & Kumar, “A deep learning-based cryptocurrency price prediction scheme for financial institutions,” J. Inf. Secur. Appl., vol. 55, p. 102583, 2020.

M. A. T. Appati, J. K., Denwar, I. W., Owusu, E., & Soli, “Construction of an ensemble scheme for stock price prediction using deep learning techniques,” Int. J. Intell. Inf. Technol., vol. 17, no. 2, pp. 72–95, 2021.

L. M. Rasdi Rere, M. I. Fanany, and A. M. Arymurthy, “Metaheuristic Algorithms for Convolution Neural Network,” Comput. Intell. Neurosci., vol. 2016, 2016.

V. Ayumi, L. M. R. Rere, M. I. Fanany, and A. M. Arymurthy, “Optimization of convolutional neural network using microcanonical annealing algorithm,” 2016 Int. Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2016, pp. 506–511, 2017.

K. Pearson, “Notes on Regression and Inheritance in the Case of Two Parents Proceedings of the Royal Society of London,” vol. 58, pp. 240–242, 2015.

R. Conlin, K. Erickson, J. Abbate, and E. Kolemen, “Keras2c: A library for converting Keras neural networks to real-time compatible C,” Eng. Appl. Artif. Intell., vol. 100, 2021.

E. Sutanto, H. Abror, Y. Gita, Y. Yhuwana, and M. Aziz, “T HRESHOLD V OLTAGE FOR D IGITAL R ESIDUAL C URRENT C IRCUIT,” 2021.

L. J. Tashman, “Out-of-sample tests of forecasting accuracy: An analysis and review,” Int. J. Forecast., vol. 16, no. 4, pp. 437–450, 2000.

P. Waldmann, G. Mészáros, B. Gredler, C. Fuerst, and J. Sölkner, “Evaluation of the lasso and the elastic net in genome-wide association studies,” Front. Genet., vol. 4, no. DEC, 2013.

W. Hastomo, A. S. Bayangkari Karno, N. Kalbuana, A. Meiriki, and Sutarno, “Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia,” J. Phys. Conf. Ser., vol. 1933, no. 1, p. 012050, 2021.

P. W. Khan, Y. C. Byun, and N. Park, “IoT-blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning,” Sensors (Switzerland), vol. 20, no. 10, pp. 1–24, 2020.

L. Liu, R. C. Chen, and S. Zhu, “Impacts of weather on short-term metro passenger flow forecasting using a deep LSTM neural network,” Appl. Sci., vol. 10, no. 8, 2020.

X. Li, C. Wang, X. Huang, and Y. Nie, “A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming,” pp. 1–11, 2020.

C. Bai, “AGA-LSTM: An Optimized LSTM Neural Network Model Based on Adaptive Genetic Algorithm,” J. Phys. Conf. Ser., vol. 1570, p. 012011, 2020.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” pp. 1–9, 2014.

A. S. B. Karno, W. Hastomo, & Arif, D., and E. S. Moreta, “Optimasi Portofolio Dan Prediksi Cryptocurrency Menggunakandeep Learning Dalam Bahasa Python,” vol. 4, no. September, 2020.




DOI: https://doi.org/10.26418/jp.v7i2.47411

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