Comparison of MDKA Stock Price Prediction using Multi-Layer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit

  • Bastul Wajhi Akramunnas Institut Teknologi Bisnis Riau, Pekanbaru, Riau
  • Legisnal Hakim Program Studi Teknik Mesin, Fakultas Teknik, Universitas Muhammadiyah Riau
  • Dita Marta Putri Institut Teknologi Bisnis Riau, Pekanbaru, Riau
  • Fahrizal Fahrizal Institut Teknologi Bisnis Riau, Pekanbaru, Riau
  • Asde Rahmawati Institut Teknologi Bisnis Riau, Pekanbaru, Riau
  • Yoan Purbolingga Institut Teknologi Bisnis Riau, Pekanbaru, Riau
Keywords: Prediction, Share, MLP, LSTM, GRU

Abstract

Shares are rights owned by a person against a company due to the delivery of capital, either in part or in whole. Investors invest in stocks and try to get maximum results, but many investors are still unsure about the risks involved in investing. To minimize risk, investors need to predict stock prices with an accurate method. Several methods that can be implemented to predict stock data include Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The research objective to be achieved in this study is to compare the performance of each algorithm in producing a more accurate stock price forecasting model by testing neurons (10, 20, 30) and epochs (50, 75, 100). The research was conducted on the stock price data of PT. Merdeka Copper Gold Tbk (MDKA) which is a mining sector share with the largest capitalization value. Tests on some of the algorithms above got the best results using 82% training data and 18% test data, namely the MLP model with 10 neurons and 100 epochs with a MAPE training data result of 2.325 and a MAPE test data of 2.014. Based on the test results, MLP can predict MDKA stock prices for the 2018-2022 period with good performance and a relatively small error rate, while tests using the LSTM and GRU methods still produce large errors. Thus, it can be concluded that MLP can predict stock prices with more accurate results.

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References

Gunawan H. Investor Milenial dan Generasi Z Dominasi Pertumbuhan Pasar Modal Indonesia [Internet]. 2021. [Online]: https://www.tribunnews.com/bisnis/2021/12/26/investor-milenial-dan-generasi-z-dominasi-pertumbuhan-pasar-modal-indonesia

Hariadi M, Muhammad AA, Nugroho SMS. Prediction of Stock Prices Using Markov Chain Monte Carlo. CENIM 2020 - Proceeding Int Conf Comput Eng Network, Intell Multimed 2020. 2020;(Cenim 2020):385–90.

Brownlee J. How to Configure the Number of Layers and Nodes in a Neural Network [Internet]. 2018. [Online]: https://machinelearningmastery.com/how-to-configure-the-number-of-layers-and-nodes-in-a-neural-network/

Karn U. A Quick Introduction to Neural Networks [Internet]. 2016. [Online]: https://www.kdnuggets.com/2016/11/quick-introduction-neural-networks.html

Siregar MNH. Model Arsitektur Artificial Neural Network pada Pelanggan Listrik Negara (PLN). InfoTekJar (Jurnal Nas Inform dan Teknol Jaringan). 2018;3(1).

Nurachim RI. Pemilihan Model Prediksi Indeks Harga Saham Yang Dikembangkan Berdasarkan Algoritma Support Vector Machine (SVM) atau Multilayer Perceptron(MLP) Studi Kasus : Saham PT Telekomunikasi Indonesia TBK. J Teknol Inform dan Komput. 2019;5(1).

Ekawijana A, Wisnuadhi B. Performansi Prediksi menggunakan Multilayer Perceptron pada Indeks Saham Gabungan Dow Jones. J Politek Negeri Bandung [Internet]. 2014;1–4. [Online]:

https://digilib.polban.ac.id/files/disk1/262/jbptppolban-gdl-ardhianeka-13077-1-performa-s.pdf

Arfan A, ETP L. Prediksi harga saham di Indonesia menggunakan algoritma long short-term memory. SeNTIK. 2019;3(1).

Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, Antika Zahrotul Kamalia. Perbandingan algoritma linear regression, LSTM, dan GRU dalam memprediksi harga saham dengan model time series. Seminastika. 2021;3(1).

Chung J, Gulcehre C, Cho K, Bengio Y. Gated feedback recurrent neural networks. In: 32nd International Conference on Machine Learning, ICML 2015. 2015.

Putro B, Furqon MT, Wijoyo SH. Prediksi Jumlah Kebutuhan Pemakaian Air Menggunakan Metode Exponential Smoothing. J Pengemb Teknol Inf dan Ilmu Komput. 2018;2(11).

Published
2023-06-26
How to Cite
Wajhi Akramunnas, B., Hakim, L., Marta Putri, D., Fahrizal, F., Rahmawati, A., & Purbolingga, Y. (2023). Comparison of MDKA Stock Price Prediction using Multi-Layer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit . Jurnal Surya Teknika, 10(1), 738-743. https://doi.org/10.37859/jst.v10i1.5004
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