(Universitas Islam Riau, Indonesia)(2) * Alfian Ma'arif
(Universitas Ahmad Dahlan, Indonesia)(3) Asno Azzawagama Firdaus
(Universitas Qamarul Huda Badaruddin Bagu, Indonesia)(4) Iswanto Suwarno
(Universitas Muhammadiyah Yogyakarta, Indonesia)*corresponding author
AbstractStocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.
KeywordsPrediction; Hybrid LSTM; Stock Price; Deep Learning; RNN Method
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DOIhttps://doi.org/10.31763/ijrcs.v4i3.1489 |
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References
[1] N. A. Sansa, “The Impact of the COVID-19 on the Financial Markets: Evidence from China and USA,†Electronic Research Journal of Social Sciences and Humanities, vol. 2, no. 2, 2020, https://dx.doi.org/10.2139/ssrn.3567901.
[2] K. F. Luchtenberg and Q. V. Vu, “The 2008 financial crisis: Stock market contagion and its determinants,†Research in International Business and Finance, vol. 33, pp. 178-203, 2015, https://doi.org/10.1016/j.ribaf.2014.09.007.
[3] W. Li et al., “The nexus between COVID-19 fear and stock market volatility,†Economic Research-Ekonomska Istraživanja, vol. 35, no. 1, pp. 1765-1785, 2022, https://doi.org/10.1080/1331677X.2021.1914125.
[4] K. Kvetenadze, “Dual-Class Share Structure: A Challenge for Contemporary Corporate Law,†Journal of Law, no. 1, pp. 124-146, 2022, https://doi.org/10.48614/jlaw.1.2022.4944.
[5] E. Tujuba, “Bearer Share Under Ethiopian Share Company Laws: Abolishing or the other option?,†Wallaga University Journal of Law, vol. 1, no. 2, pp. 33-51, 2024, https://doi.org/10.20372/wjlaw.v1i2.1072.
[6] T. H. Kaisanlahti, “Negotiability of Shares – against the interest of “true owner�,†European Property Law Journal, vol. 11, no. 3, pp. 167-194, 2022, https://doi.org/10.1515/eplj-2022-0010.
[7] Z. Z. K. Mada, “Analysis of Decisions of The General Meeting of Shareholders That Have a Balanced Percentage of Share Ownership,†Veteran Law Review, vol. 6, no. 1, pp. 74-91, 2023, https://doi.org/10.35586/velrev.v6i1.4687.
[8] A. Gutterman, “Shareholders,†SSRN Electronic Journal, pp. 1-59, 2023, https://dx.doi.org/10.2139/ssrn.4525600.
[9] M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting: A literature review,†Expert Systems with Applications, vol. 197, p. 116659, 2022, https://doi.org/10.1016/j.eswa.2022.116659.
[10] X. Li, P. Wu, and W. Wang, “Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong,†Information Processing & Management, vol. 57, no. 5, p. 102212, 2020, https://doi.org/10.1016/j.ipm.2020.102212.
[11] A. W. Li and G. S. Bastos, “Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review,†IEEE Access, vol. 8, pp. 185232-185242, 2020, https://doi.org/10.1109/ACCESS.2020.3030226.
[12] A. T. Oyewole, O. B. Adeoye, W. A. Addy, C. C. Okoye, O. C. Ofodile, and C. E. Ugochukwu, “Predicting Stock Market Movement using Neural Networks: A Review and Application Study,†Computer Science & IT Research Journal, vol. 5, no. 3, pp. 651-670, 2024, https://doi.org/10.51594/csitrj.v5i3.912.
[13] P. Whig, P. Sharma, A. B. Bhatia, R. R. Nadikattu, and B. Bhatia, “Machine Learning and its Role in Stock Market Prediction,†Deep Learning Tools for Predicting Stock Market Movements, pp. 271-297, 2024, https://doi.org/10.1002/9781394214334.ch12.
[14] A. I. Al-Alawi and N. Alshakhoori, “Stock Price Prediction Using Artificial Intelligence: A Literature Review,†2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), pp. 1-6, 2024, https://doi.org/10.1109/ICETSIS61505.2024.10459442.
[15] M. T. Mia et al., “Dominance of External Features in Stock Price Prediction in a Predictable Macroeconomic Environment,†Journal of Business and Management Studies, vol. 5, no. 6, pp. 128-133, 2023, https://doi.org/10.32996/jbms.2023.5.6.10.
[16] A. Njegovanović, “Financial Evolution and Interdisciplinary Research,†Financial Markets, Institutions and Risks, vol. 7, no. 1, pp. 71-95, 2023, https://doi.org/10.21272/fmir.7(1).71-95.2023.
[17] R. Lu and M. Lu, “Stock Trend Prediction Algorithm Based on Deep Recurrent Neural Network,†Wireless Communications and Mobile Computing, vol. 2021, no. 1, pp. 1-10, 2021, https://doi.org/10.1155/2021/5694975.
[18] D. Duxbury, T. Gärling, A. Gamble, and V. Klass, “How emotions influence behavior in financial markets: a conceptual analysis and emotion-based account of buy-sell preferences,†The European Journal of Finance, vol. 26, no. 14, pp. 1417-1438, 2020, https://doi.org/10.1080/1351847X.2020.1742758.
[19] R. Jain, D. Sharma, A. Behl, and A. K. Tiwari, “Investor personality as a predictor of investment intention – mediating role of overconfidence bias and financial literacy,†International Journal of Emerging Markets, vol. 18, no. 12, pp. 5680-5706, 2023, https://doi.org/10.1108/IJOEM-12-2021-1885.
[20] A. Quaicoe and P. Q. Eleke-Aboagye, “Behavioral factors affecting investment decision-making in bank stocks on the Ghana stock exchange,†Qualitative Research in Financial Markets, vol. 13, no. 4, pp. 425–439, 2021, https://doi.org/10.1108/QRFM-05-2020-0084.
[21] S. Talwar, M. Talwar, V. Tarjanne, and A. Dhir, “Why retail investors traded equity during the pandemic? An application of artificial neural networks to examine behavioral biases,†Psychology and Marketing, vol. 38, no. 11, pp. 2142-2163, 2021, https://doi.org/10.1002/mar.21550.
[22] E. Rokhsatyazdi, S. Rahnamayan, H. Amirinia and S. Ahmed, “Optimizing LSTM Based Network For Forecasting Stock Market,†2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1-7, 2020, https://doi.org/10.1109/CEC48606.2020.9185545.
[23] 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,†Decision Analytics Journal, vol. 2, p. 100015, 2022, https://doi.org/10.1016/j.dajour.2021.100015.
[24] A. Pranolo, Y. Mao, A. P. Wibawa, A. B. P. Utama, and F. A. Dwiyanto, “Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction,†Knowledge Engineering and Data Science, vol. 5, no. 1, pp. 53-63, 2022, http://dx.doi.org/10.17977/um018v5i12022p53-66.
[25] G. V. Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,†Artificial Intelligence Review, vol. 53, pp. 5929-5955, 2020, https://doi.org/10.1007/s10462-020-09838-1.
[26] J. Duan, P. Zhang, R. Qiu, and Z. Huang, “Long short-term enhanced memory for sequential recommendation,†World Wide Web, vol. 26, pp. 561-583, 2023, https://doi.org/10.1007/s11280-022-01056-9.
[27] M. Kowsher et al., “LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy,†Procedia Computer Science, vol. 193, pp. 131-140, 2021, https://doi.org/10.1016/j.procs.2021.10.013.
[28] M. Alizamir et al., “Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform,†Engineering Applications of Artificial Intelligence, vol. 123, p. 106199, 2023, https://doi.org/10.1016/j.engappai.2023.106199.
[29] A. Agga, A. Abbou, M. Labbadi, Y. E. Houm, and I. H. O. Ali, “CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production,†Electric Power Systems Research, vol. 208, p. 107908, 2022, https://doi.org/10.1016/j.epsr.2022.107908.
[30] H. Widiputra, A. Mailangkay, and E. Gautama, “Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction,†Complexity, vol. 2021, no. 1, pp. 1-14, 2021, https://doi.org/10.1155/2021/9903518.
[31] L. Sun, W. Xu, and J. Liu, “Two-channel Attention Mechanism Fusion Model of Stock Price Prediction Based on CNN-LSTM,†ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 20, no. 5, pp. 1-12, 2021, https://doi.org/10.1145/3453693.
[32] H. Song and H. Choi, “Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models,†Applied Sciences, vol. 13, no. 7, p. 4644, 2023, https://doi.org/10.3390/app13074644.
[33] W. Xu, “Stock Price Prediction based on CNN-LSTM Model in the PyTorch Environment,†Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), pp. 1272-1276, 2022, https://doi.org/10.2991/978-94-6463-036-7_188.
[34] P. Singh, M. Jha, M. Sharaf, M. A. El-Meligy and T. R. Gadekallu, “Harnessing a Hybrid CNN-LSTM Model for Portfolio Performance: A Case Study on Stock Selection and Optimization,†IEEE Access, vol. 11, pp. 104000-104015, 2023, https://doi.org/10.1109/ACCESS.2023.3317953.
[35] A. H. Bukhari, M. A. Z. Raja, M. Sulaiman, S. Islam, M. Shoaib and P. Kumam, “Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting,†IEEE Access, vol. 8, pp. 71326-71338, 2020, https://doi.org/10.1109/ACCESS.2020.2985763.
[36] C. Bulut and B. Hüdaverdi, “Hybrid Approaches in Financial Time Series Forecasting: A Stock Market Application,†Ekoist: Journal of Econometrics and Statistics, no. 37, pp. 53-68, 2022, https://doi.org/10.26650/ekoist.2022.37.1108411.
[37] H. Yeng and M. Siahaan, “Perancangan Sistem Prediksi Harga Saham Berbasis Website Menggunakan Algoritma Hybrid (ARIMA-LSTM),†Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 13, no. 1, pp. 61-72, 2024, https://doi.org/10.35889/jutisi.v13i1.1620.
[38] G. Kumar, U. P. Singh, and S. Jain, “An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting,†Soft computing, vol. 26, pp. 12115-12135, 2022, https://doi.org/10.1007/s00500-022-07451-8.
[39] S. Chen and C. Zhou, “Stock Prediction Based on Genetic Algorithm Feature Selection and Long Short-Term Memory Neural Network,†IEEE Access, vol. 9, pp. 9066-9072, 2021, https://doi.org/10.1109/ACCESS.2020.3047109.
[40] X. Zeng, J. Cai, C. Liang, and C. Yuan, “A hybrid model integrating long short-term memory with adaptive genetic algorithm based on individual ranking for stock index prediction,†PLoS One, vol. 17, no. 8, p. e0272637, 2022, https://doi.org/10.1371/journal.pone.0272637.
[41] S. Dutta et al., “A Novel Hybrid Model Using Lstm and Rnn for Stock Market Prediction,†International Journal of Engineering Research & Technology (IJERT), vol. 13, no. 4, 2024, https://doi.org/10.17577/IJERTV13IS040184.
[42] A. Khalil, “Using Hybrid Machine learning Models for Stock Price Forecasting and Trading,†AUC Knowledge Fountain, 2024, https://fount.aucegypt.edu/etds/2285.
[43] Y. Ding, “Enhancing Stock Price Prediction Method Based on CNN-LSTM hybrid model,†Highlights in Business, Economics and Management, vol. 21, pp. 774-781, 2023, https://doi.org/10.54097/hbem.v21i.14760.
[44] M. A. Hossain, R. Karim, R. Thulasiram, N. D. B. Bruce and Y. Wang, “Hybrid Deep Learning Model for Stock Price Prediction,†2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1837-1844, 2018, https://doi.org/10.1109/SSCI.2018.8628641.
[45] Y. Yujun, Y. Yimei, and X. Jianhua, “A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD,†Complexity, vol. 2020, no. 1, pp. 1-16, 2020, https://doi.org/10.1155/2020/6431712.
[46] A. Pranolo et al., “Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting,†Knowledge Engineering and Data Science, vol. 7, no. 1, pp. 1-12, 2024, https://doi.org/10.17977/um018v7i12024p1-12.
[47] A. K. S. Lenson and G. Airlangga, “Comparative Analysis of MLP , CNN , and RNN Models in Automatic Speech Recognition : Dissecting Performance Metric,†Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 4, pp. 576-583, 2023, https://doi.org/10.12928/biste.v5i4.9668.
[48] D. Pebrianti, H. Kurniawan, L. Bayuaji, and R. Rusdah, “XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 4, pp. 1179-1195, 2024, http://dx.doi.org/10.26555/jiteki.v9i4.27712.
[49] A. P. Wibawa et al., “Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting,†Knowledge Engineering and Data Science, vol. 6, no. 2, pp. 170-187, 2023, https://doi.org/10.17977/um018v6i22023p170-187.
[50] K. Y. Halim, D. T. Nugrahadi, M. R. Faisal, R. Herteno, and I. Budiman, “Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 3, pp. 606-618, 2023, http://dx.doi.org/10.26555/jiteki.v9i3.26354.
[51] W. T. Handoko and A. N. Handayani, “Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 3, pp. 649-660, 2023, http://dx.doi.org/10.26555/jiteki.v9i3.26395.
[52] N. M. Azahra and E. B. Setiawan, “Sentence-Level Granularity Oriented Sentiment Analysis of Social Media Using Long Short-Term Memory (LSTM) and IndoBERTweet Method,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 1, pp. 85-95, 2023, http://dx.doi.org/10.26555/jiteki.v9i1.25765.
[53] T. A. Armanda, I. P. Wardhani, T. M. Akhriza, and T. M. A. Admira, “Recurrent Session Approach to Generative Association Rule based Recommendation,†Knowledge Engineering and Data Science, vol. 6, no. 2, pp. 199-214, 2023, https://doi.org/10.17977/um018v6i22023p199-214.
[54] W. K. Sari, D. P. Rini, and R. F. Malik, “Text Classification Using Long Short-Term Memory With GloVe Features,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 5, no. 2, pp. 85-100, 2020, https://doi.org/10.26555/jiteki.v5i2.15021.
[55] M. F. Maulana, S. Sa’adah, and P. E. Yunanto, “Crude Oil Price Forecasting Using Long Short-Term Memory,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 7, no. 2, pp. 286-295, 2021, http://dx.doi.org/10.26555/jiteki.v7i2.21086.
[56] M. Hayaty and A. H. Pratama, “Performance of Lexical Resource and Manual Labeling on Long Short-Term Memory Model for Text Classification,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 1, pp. 74-84, 2023, http://dx.doi.org/10.26555/jiteki.v9i1.25375.
[57] G. Airlangga, “Performance Evaluation of Deep Learning Techniques in Gesture Recognition Systems,†Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 1, pp. 83-90, 2024, https://doi.org/10.12928/biste.v6i1.10120.
[58] Y. Sujatna et al., “Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks,†Knowledge Engineering and Data Science, vol. 6, no. 2, pp. 215-230, 2023, http://dx.doi.org/10.17977/um018v6i22023p215-250.
[59] Y. Zhang, B. Yan, and M. Aasma, “A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM,†Expert Systems with Applications, vol. 159, p. 113609, 2020, https://doi.org/10.1016/j.eswa.2020.113609.
[60] M. U. Salur and I. Aydin, “A Novel Hybrid Deep Learning Model for Sentiment Classification,†IEEE Access, vol. 8, pp. 58080-58093, 2020, https://doi.org/10.1109/ACCESS.2020.2982538.
[61] C. N. Dang, M. N. Moreno-GarcÃa, and F. D. L. Prieta, “Hybrid Deep Learning Models for Sentiment Analysis,†Complexity, vol. 2021, no. 1, pp. 1-16, 2021, https://doi.org/10.1155/2021/9986920.
[62] A. Kurniawati, E. M. Yuniarno, and Y. K. Suprapto, “Deep Learning for Multi-Structured Javanese Gamelan Note Generator,†Knowledge Engineering and Data Science, vol. 6, no. 1, pp. 41-56, 2023, https://doi.org/10.17977/um018v6i12023p41-56.
[63] D. M. Bui, P. D. Le, M. T. Cao, T. T. Pham, and D. A. Pham, “Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method,†International Journal of Green Energy, vol. 17, no. 7, pp. 382-406, 2020, https://doi.org/10.1080/15435075.2020.1761810.
[64] G. Marulanda, J. Cifuentes, A. Bello, and J. Reneses, “A hybrid model based on LSTM neural networks with attention mechanism for short-term wind power forecasting,†Wind Engineering, 2023, https://doi.org/10.1177/0309524X231191163.
[65] I. H. Arsytania, E. B. Setiawan, and I. Kurniawan, “Movie Recommender System with Cascade Hybrid Filtering Using Convolutional Neural Network,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 4, pp. 1262-1274, 2024, http://dx.doi.org/10.26555/jiteki.v9i4.28146.
[66] D. S. K. Karunasingha, “Root mean square error or mean absolute error? Use their ratio as well,†Information Sciences, vol. 585, pp. 609-629, 2022, https://doi.org/10.1016/j.ins.2021.11.036.
[67] E. Sitompul, R. M. Putra, H. Tarigan, A. Silitonga, and I. Bukhori, “Implementation of Digital Feedback Control with Change Rate Limiter in Regulating Water Flow Rate Using Arduino,†Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 1, pp. 72-82, 2024, https://doi.org/10.12928/biste.v6i1.10234.
[68] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,†PeerJ Computer Science, vol. 7, p. e623, 2021, https://doi.org/10.7717/peerj-cs.623.
[69] H. D. Trung, “Estimation of Crowd Density Using Image Processing Techniques with Background Pixel Model and Visual Geometry Group,†Buletin Ilmiah Sarjana Teknik Elektro, vol. 6, no. 2, pp. 142-154, 2024, https://doi.org/10.12928/biste.v6i1.10234.
[70] B. Hanif, A. Larasati, R. Nurdiansyah, and T. Le, “The Effect of the Number of Hidden Layers on The Performance of Deep Q-Network for Traveling Salesman Problem,†Knowledge Engineering and Data Science, vol. 6, no. 2, pp. 188-198, 2023, https://doi.org/10.17977/um018v6i22023p188-198.
[71] M. Ćalasan, S. H. E. Abdel Aleem, and A. F. Zobaa, “On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function,†Energy Conversion and Management, vol. 210, p. 112716, 2020, https://doi.org/10.1016/j.enconman.2020.112716.
[72] T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,†Geoscientific Model Development, vol. 15, no. 14, pp. 5481-5487, 2022, https://doi.org/10.5194/gmd-15-5481-2022.
[73] A. G. Putrada, N. Alamsyah, I. D. Oktaviani, and M. N. Fauzan, “A Hybrid Genetic Algorithm-Random Forest Regression Method for Optimum Driver Selection in Online Food Delivery,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 4, pp. 1060-1079, 2023, http://dx.doi.org/10.26555/jiteki.v9i4.27014.
[74] B. D. Satoto, R. T. Wahyuningrum, and B. K. Khotimah, “Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 2, pp. 348-362, 2023, http://dx.doi.org/10.26555/jiteki.v9i2.26222.
[75] A. A. Waskita, S. Yushady, C. H. Bissa, I. A. Satya, and R. S. Alwi, “Development of Novel Machine Learning to Optimize the Solubility of Azathioprine as Anticancer Drug in Supercritical Carbon Dioxide,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 1, pp. 49-57, 2023, http://dx.doi.org/10.26555/jiteki.v9i1.25608.
[76] H. Aini and H. Haviluddin, “Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach,†Knowledge Engineering and Data Science, vol. 2, no. 1, pp. 1-9, 2019, http://dx.doi.org/10.17977/um018v2i12019p1-9.
[77] A. Azhari, A. Susanto, A. Pranolo, and Y. Mao, “Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities,†Knowledge Engineering and Data Science, vol. 2, no. 2, pp. 47-57, 2019, https://doi.org/10.17977/um018v2i22019p47-57.
[78] D. N. Muhammady, H. A. E. Nugraha, V. R. S. Nastiti, and C. S. K. Aditya, “Students Final Academic Score Prediction Using Boosting Regression Algorithms,†Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 1, pp. 154-165, 2024, http://dx.doi.org/10.26555/jiteki.v10i1.28352.
[79] P. Purnawansyah, H. Haviluddin, H. Darwis, H. Azis, and Y. Salim, “Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction,†Knowledge Engineering and Data Science, vol. 4, no. 1, pp. 14-28, 2021, http://dx.doi.org/10.17977/um018v4i12021p14-28.
[80] M. K. Ho, H. Darman, S. Musa, “Stock price prediction using ARIMA, neural network and LSTM models,†Journal of Physics: Conference Series, vol. 1988, no. 1, p. 012041, 2021, https://doi.org/10.1088/1742-6596/1988/1/012041.
[81] E. J. Prasetyo and K. D. Hartomo, “Multi-industry stock forecasting using GRU-LSTM deep transfer learning method,†Jurnal INFOTEL, vol. 15, no. 2, pp. 30-43, 2023, https://doi.org/10.20895/infotel.v15i2.941.
[82] D. V. Trivedi and S. Patel, “An Analysis of GRU-LSTM Hybrid Deep Learning Models for Stock Price Prediction,†International Journal of Scientific Research in Science, Engineering and Technology, vol. 93, no. 3, pp. 47-51, 2022, https://doi.org//10.32628/IJSRSET229264.
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Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia