Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance


Sekhudin Sekhudin(1); Yuli Purwati(2*); Fandy Setyo Utomo(3); Mohd Sanusi Azmi(4); Pungkas Subarkah(5);

(1) Universitas AMIKOM Purwokerto
(2) Universitas AMIKOM Purwokerto
(3) Universitas AMIKOM Purwokerto
(4) Universiti Teknikal Malaysia Melaka
(5) Universitas AMIKOM Purwokerto
(*) Corresponding Author

  

Abstract


A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.

Keywords


Closing Price; Intelligent System; Investment; Machine Learning; Supervised Learning

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 193 times
PDF view: 62 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v15i2.1586.271-282 		  

Cite

How to cite item

References


I. Sudarman and N. Diana, “The Effect of Financial Ratios on Sharia Stock Prices in Companies in the LQ45 Index 2020-2021,” J. Ilm. Ekon. Islam, vol. 8, no. 1, p. 117, 2022.

J. Jefry and A. Djazuli, “The Effect of Inflation, Interest Rates and Exchange Rates on Stock Prices of Manufacturing Companies in Basic and Chemical Industrial Sectors on the Indonesia Stock Exchange (IDX),” Int. J. Business, Manag. Econ. Res., vol. 1, no. 1, pp. 34–49, 2020.

S. M. Prabin and M. S. Thanabal, “A repairing artificial neural network model-based stock price prediction,” Int. J. Comput. Intell. Syst., vol. 14, no. 1, pp. 1337–1355, 2021.

S. S. Abubaker and S. R. Farid, “Stock Market Prediction Using LSTM,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 8.5.2017, pp. 2003–2005, 2022.

P. Chhajer, M. Shah, and A. Kshirsagar, “The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction,” Decis. Anal. J., vol. 2, no. November 2021, p. 100015, 2022.

M. Jufri, “Implementation of Artificial Neural Network in Predicting Birth Rate in Batam City Using Backpropagation Method,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 8, no. 1, pp. 85–94, 2021.

Melina, Sukono, H. Napitupulu, A. Sambas, A. Murniati, and V. A. Kusumaningtyas, “Artificial Neural Network-Based Machine Learning Approach to Stock Market Prediction Model on the Indonesia Stock Exchange During the COVID-19,” Eng. Lett., vol. 30, no. 3, pp. 988–1000, 2022.

S. S. Al Al-Hasnawi and L. H. Al Al-Hchemi, “Stock Closing Price Prediction of ISX-listed Industrial Companies Using Artificial Neural Networks,” Am. J. Bus. Oper. Res., vol. 6, no. 2, pp. 47–55, 2022.

O. M. A. AL-atraqchi, “A Proposed Model for Build a Secure Restful API to Connect between Server Side and Mobile Application Using Laravel Framework with Flutter Toolkits,” Cihan Univ. Sci. J., vol. 6, no. 2, pp. 28–35, 2022.

S. Mowla and S. V Kolekar, “Development and integration of E-learning services using rest APIs,” Int. J. Emerg. Technol. Learn., vol. 15, no. 4, pp. 53–72, 2020.

G. Susrama, M. Diyasa, G. S. Budiwitjaksono, H. Amarul, and I. Ade, “Comparative Analysis of Rest and GraphQL Technology on Nodejs-Based Api Development,” vol. 2021, pp. 1–9, 2021.

U. Singh, “REST API Framework : Designing and Developing Web Services,” Int. Res. J. Eng. Technol., vol. 8, no. June, pp. 815–817, 2021.

S. Y. M, S. H. R, and R. Nagapadma, “Survey Paper: Framework of REST APIs,” Int. Res. J. Eng. Technol., no. June, pp. 1115–1119, 2020.

M. A. Khder, “Web scraping or web crawling: State of art, techniques, approaches and application,” Int. J. Adv. Soft Comput. its Appl., vol. 13, no. 3, pp. 144–168, 2021.

K. BABU, “Survey on Web scraping technology,” Waffen-Und Kostumkd. J., vol. 16, no. 06, pp. 1–8, 2020.

C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, no. March, pp. 1–17, 2021.

A. P. Joshi and B. V Patel, “Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process,” Orient. J. Comput. Sci. Technol., vol. 13, no. 0203, pp. 78–81, 2021.

A. G. Farizawani, M. Puteh, Y. Marina, and A. Rivaie, “A review of artificial neural network learning rule based on multiple variant of conjugate gradient approaches,” J. Phys. Conf. Ser., vol. 1529, no. 2, 2020.

A. Duykuluoğlu, “The Significance of Artificial Neural Networks in Educational Research : A Summary of Research and Literature,” vol. 2, no. 2, pp. 107–116, 2021.

R. R. Waliyansyah and N. D. Saputro, “Forecasting New Student Candidates Using the Random Forest Method,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 11, no. 1, p. 44, 2020.

J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C. H. Lee, “On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression,” IEEE Signal Process. Lett., vol. 27, pp. 1485–1489, 2020.

M. del R. C. Estrada, M. E. G. Camarillo, M. E. S. Parraguirre, M. E. G. Castillo, E. M. Juárez, and M. J. C. Gómez, “Evaluation of Several Error Measures Applied to the Sales Forecast System of Chemicals Supply Enterprises,” Int. J. Bus. Adm., vol. 11, no. 4, p. 39, 2020.

C. C. Aggarwal, Neural Networks and Deep Learning. 2018.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Sekhudin Sekhudin, Yuli Purwati, Fandy Setyo Utomo, Mohd Sanusi Azmi, Pungkas Subarkah

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