PREDIKSI INDEKS SAHAM SYARIAH MENGGUNAKAN MODEL LONG SHORT-TERM MEMORY (LSTM)
DOI:
https://doi.org/10.33474/jimmu.v9i1.21547Keywords:
Prediksi, JII70, LSTMAbstract
Prediksi indeks saham syariah saat ini menjadi hal yang krusial bagi para investor dan analis keuangan. Jakarta Islamic Index 70 (Indeks JII70) adalah indeks saham syariah dengan jumlah perusahaan terbanyak. Hal ini menjadi sangat menarik untuk melakukan prediksi indeks saham JII70. Tujuan penelitian ini adalah memprediksi indeks saham syariah JII70 menggunakan model Long Short-Term Memory (LSTM). Data yang diguakan dalam penelitian ini adalah data historis closed indeks saham syariah JII70 empat tahun terakhir. Hasil penelitian menunjukkan bahwa model LSTM baik digunakan untuk meprediksi indeks saham syariah JII70 dengan nilai RMSE sebesar 6,8993. Penelitian ini sangat penting bagi investor dalam menetukan keputusan investasi dan strategi perdagangan saham.
References
Aehir, Zinnet Duygu et al. 2020. “Stocks Prices Prediction with Long Short-Term Memory.” IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security 0: 221–26.
Christy Jackson, J., J. Prassanna, Md Abdul Quadir, and V. Sivakumar. 2022. “Stock Market Analysis and Prediction Using Time Series Analysis.” Materials Today: Proceedings (July).
Gülmez, Burak. 2023. “Stock Price Prediction with Optimized Deep LSTM Network with Artificial Rabbits Optimization Algorithm.” Expert Systems with Applications 227(May): 120346. https://doi.org/10.1016/j.eswa.2023.120346.
Haryono, Slamet, and Bella Atika. 2023. 8 International Journal of Professional Business Review Analyzing the Dynamics of Islamic Stock Market Indices in Several Muslim Countries.
He, Bo, Enyu Gong, Longbing Li, and Yongfen Yang. 2023. “A Stock Price Prediction Method Based on LSTM and K-Means.” Frontiers in Science and Engineering 3(6): 44–57.
Islam, Md Ashraful, Md Rana Sikder, Sayed Mohammed Ishtiaq, and Abdus Sattar. 2023. “Stock Market Prediction of Bangladesh Using Multivariate Long Short-Term Memory with Sentiment Identification.” International Journal of Electrical and Computer Engineering 13(5): 5696–5706.
Liu, Ziji. 2023. “Stock Price Prediction Based on Long Short-Term Memory Model.” Highlights in Science, Engineering and Technology 39: 651–56.
Malsa, Nitima, Vaibhav Vyas, and Jyoti Gautam. 2021. “RMSE Calculation of LSTM Models for Predicting Prices of Different Cryptocurrencies.” International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-021-01431-1.
Moh. Asra. 2023. “Stock in the Sharia Economic Perspective.” Review of Islamic Studies 2(1): 21–26.
Mohanty, Saumendra. 2023. “An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the Prediction of Stock and Forex Markets.” International Journal For Multidisciplinary Research 5(3): 1–9.
Nurmalitasari, Nurmalitasari, Sri Sumarlinda, Nyoto Supriyanto, and Davina Kusuma Putri. 2020. “LQ45 Stock Price Predictions Using The Deep Learning Method.” International Journal of Advanced Research and Publications 4(4): 20–23. http://www.ijarp.org/paper-details.php?ref_number=RP0320-3300.
Sama, Hayder Abdulhussein AlHakeem, Jasim Al-Anber Nashaat, and Abdulzahra Atee Hayfaa. 2023. “Iraqi Stock Market Prediction Using Artificial Neural Network and Long Short-Term Memory.” Journal of Techniques 5(1): 9–15.
Sharma, Yash, Ankit Kumar, Varun Dubey, and Vipin Rai. 2023. “Stock Price Prediction Using LSTM.” 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023 44: 302–6.
Srinidhi, R., S. Siddharth Sarathy, and A. Ponraj. 2023. “Stock Market Prediction Using Machine Learning Algorithm.” Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023 12(4): 141–45.
Supriani, Indri, Sri Herianingrum, Sri Yayu Ninglasari, and Ryan Setya Budi. 2022. “Islamic Stock Market Performance Pre-COVID-19: Empirical Evidence from Jakarta Islamic Index.” Jurnal Ekonomi dan Bisnis Islam (Journal of Islamic Economics and Business) 8(2): 267–87.
Yan, Yijin, Xin Nie, Mingyang Wang, and Yuxin Chen. 2023. “LSTM-Based Stock Price Prediction Model Using News Sentiments.” Advances in Economics and Management Research 6(1): 57.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Jurnal Ilmu Manajemen (JIMMU)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi 4.0 Internasional.