Stock Price Forecasting with Multivariate Time Series Long Short-Term Memory: A Deep Learning Approach

Furizal Furizal, Asdelina Ritonga, Alfian Ma’arif, Iswanto Suwarno

Abstract


Stocks with their inherent complexity and dynamic nature influenced by a multitude of external and internal factors, play a crucial role in investment analysis and trend prediction. As financial instruments representing ownership in a company, stocks not only reflect the company's performance but are also affected by external factors such as economic conditions, political climates, and social changes. In a rapidly changing environment, investors and analysts continuously develop models and algorithms to aid in making timely and effective investment decisions. This study applies a Sequential model to predict stock data using a LSTM neural network. The model consists of a single hidden LSTM layer with 200 units. The LSTM layer, the core element of this model, enables it to capture temporal patterns and long-term relationships within the data. The training and testing data were divided into 80% for training and 20% for testing. The Adam optimizer was chosen to optimize the model's learning process, with a learning rate of 0.001. Dropout techniques were applied to reduce overfitting, with a dropout rate of 0.4, along with batch normalization and ReLU activation functions to enhance model performance. Additionally, callback mechanisms, including ReduceLROnPlateau and EarlyStopping, were used to optimize the training process and prevent overfitting. The model was evaluated using MAE and MSE metrics on training, testing, and future prediction data. The results indicate that the model achieved high accuracy, with an MAE of 0.0142 on the test data. However, future predictions showed higher MAE values, suggesting room for improvement in long-term forecasting. The model's ability to accurately predict future stock closing prices can assist investors in making informed investment decisions.

Keywords


LSTM; Time Series; Deep Learning; Stock Price; Forecasting.

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References


D. Mariyani, H. Hariyanti, and D. R. Novida, “The Effect Of Economic Value Added (Eva), Market Value Added (MVA), Refined Economic Value Added (REVA) On Stock Prices And Stock Returns In Manufacturing Companies Listed In Indonesia Stock Exchange,” Winter Journal: Imwi Student Research Journal, vol. 3, no. 1, pp. 10–22, Oct. 2023, doi: 10.52851/wt.v3i1.46.

A. Kartono, V. W. Fatmawati, S. T. Wahyudi, and Irmansyah, “Numerical Solution of Nonlinear Schrodinger Approaches Using the Fourth-Order Runge-Kutta Method for Predicting Stock Pricing,” J Phys Conf Ser, vol. 1491, no. 1, p. 012021, Mar. 2020, doi: 10.1088/1742-6596/1491/1/012021.

R. Wu, Z. Qin, and B.-Y. Liu, “A systemic analysis of dynamic frequency spillovers among carbon emissions trading (CET), fossil energy and sectoral stock markets: Evidence from China,” Energy, vol. 254, p. 124176, Sep. 2022, doi: 10.1016/j.energy.2022.124176.

B. Dhingra, S. Batra, V. Aggarwal, M. Yadav, and P. Kumar, “Stock market volatility: a systematic review,” Journal of Modelling in Management, vol. 19, no. 3, pp. 925–952, Mar. 2024, doi: 10.1108/JM2-04-2023-0080.

A. Firmansyah, W. Utami, H. Umar, and S. D. Mulyani, “Do Derivative Instruments Increase Firm Risk for Indonesia Non-Financial Companies?,” International Journal of Business, Economics and Management, vol. 7, no. 2, pp. 81–95, 2020, doi: 10.18488/journal.62.2020.72.81.95.

T. H. Nguyen, H. A. Nguyen, Q. C. Tran, and Q. L. Le, “Dividend policy and share price volatility: empirical evidence from Vietnam,” Accounting, pp. 67–78, 2020, doi: 10.5267/j.ac.2019.12.006.

K. Mishev, A. Gjorgjevikj, I. Vodenska, L. T. Chitkushev, and D. Trajanov, “Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers,” IEEE Access, vol. 8, pp. 131662–131682, 2020, doi: 10.1109/ACCESS.2020.3009626.

A. Thakkar and K. Chaudhari, “A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 2133–2164, Jun. 2021, doi: 10.1007/s11831-020-09448-8.

W. S. Udo, N. A. Ochuba, O. Akinrinola, and Y. J. Ololade, “Theoretical approaches to data analytics and decision-making in finance: Insights from Africa and the United States,” GSC Advanced Research and Reviews, vol. 18, no. 3, pp. 343–349, Mar. 2024, doi: 10.30574/gscarr.2024.18.3.0114.

A. Thakkar and K. Chaudhari, “Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions,” Information Fusion, vol. 65, pp. 95–107, Jan. 2021, doi: 10.1016/j.inffus.2020.08.019.

A. Thakkar and K. Chaudhari, “A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions,” Expert Syst Appl, vol. 177, p. 114800, Sep. 2021, doi: 10.1016/j.eswa.2021.114800.

M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. S., “Deep Learning for Stock Market Prediction,” Entropy, vol. 22, no. 8, p. 840, Jul. 2020, doi: 10.3390/e22080840.

A. Richards, C. C. French, A. J. Calder, B. Webb, R. Fox, and A. W. Young, “Anxiety-related bias in the classification of emotionally ambiguous facial expressions.,” Emotion, vol. 2, no. 3, pp. 273–287, 2002, doi: 10.1037/1528-3542.2.3.273.

I. Blanchette and A. Richards, “The influence of affect on higher level cognition: A review of research on interpretation, judgement, decision making and reasoning,” Cogn Emot, vol. 24, no. 4, pp. 561–595, Jun. 2010, doi: 10.1080/02699930903132496.

G. Kumar, S. Jain, and U. P. Singh, “Stock Market Forecasting Using Computational Intelligence: A Survey,” Archives of Computational Methods in Engineering, vol. 28, no. 3, pp. 1069–1101, May 2021, doi: 10.1007/s11831-020-09413-5.

X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J Pet Sci Eng, vol. 186, p. 106682, Mar. 2020, doi: 10.1016/j.petrol.2019.106682.

Y. Ji, A. W.-C. Liew, and L. Yang, “A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast,” IEEE Access, vol. 9, pp. 23660–23671, 2021, doi: 10.1109/ACCESS.2021.3056713.

G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artif Intell Rev, vol. 53, no. 8, pp. 5929–5955, Dec. 2020, doi: 10.1007/s10462-020-09838-1.

R. Huang et al., “Well performance prediction based on Long Short-Term Memory (LSTM) neural network,” J Pet Sci Eng, vol. 208, p. 109686, Jan. 2022, doi: 10.1016/j.petrol.2021.109686.

G. Ding and L. Qin, “Study on the prediction of stock price based on the associated network model of LSTM,” International Journal of Machine Learning and Cybernetics, vol. 11, no. 6, pp. 1307–1317, Jun. 2020, doi: 10.1007/s13042-019-01041-1.

A. Kothari, A. Kulkarni, T. Kohade, and C. Pawar, “Stock Market Prediction Using LSTM,” in Smart Trends in Computing and Communications, pp. 143–164, 2024, doi: 10.1007/978-981-97-1326-4_13.

R. Kumar, P. Kumar, and Y. Kumar, “Analysis of Financial Time Series Forecasting using Deep Learning Model,” in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 877–881, 2021, doi: 10.1109/Confluence51648.2021.9377158.

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, p. 41, Jul. 2023, doi: 10.17977/um018v6i12023p41-56.

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, doi: 10.12928/biste.v5i4.9668.

J. Du, Q. Liu, K. Chen, and J. Wang, “Forecasting stock prices in two ways based on LSTM neural network,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1083–1086, 2019, doi: 10.1109/ITNEC.2019.8729026.

D. H. D. Nguyen, L. P. Tran, and V. Nguyen, “Predicting Stock Prices Using Dynamic LSTM Models,” in Communications in Computer and Information Science (CCIS), pp. 199–212, 2019, doi: 10.1007/978-3-030-32475-9_15.

S. Kumar S, C. D, and S. Rajan, “Stock price prediction using deep learning LSTM (long short-term memory),” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1787–1791, 2022, doi: 10.1109/ICACITE53722.2022.9823639.

P. Hoang Vuong, T. Tan Dat, T. Khoi Mai, P. Hoang Uyen, and P. The Bao, “Stock-Price Forecasting Based on XGBoost and LSTM,” Computer Systems Science and Engineering, vol. 40, no. 1, pp. 237–246, 2022, doi: 10.32604/csse.2022.017685.

M. K. Ho, H. Darman, and S. Musa, “Stock Price Prediction Using ARIMA, Neural Network and LSTM Models,” J Phys Conf Ser, vol. 1988, no. 1, p. 012041, Jul. 2021, doi: 10.1088/1742-6596/1988/1/012041.

J. Qiu, B. Wang, and C. Zhou, “Forecasting stock prices with long-short term memory neural network based on attention mechanism,” PLoS One, vol. 15, no. 1, p. e0227222, Jan. 2020, doi: 10.1371/journal.pone.0227222.

M. Rana, Md. M. Uddin, and Md. M. Hoque, “Effects of Activation Functions and Optimizers on Stock Price Prediction using LSTM Recurrent Networks,” in Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence, pp. 354–358, 2019, doi: 10.1145/3374587.3374622.

M. Nikou, G. Mansourfar, and J. Bagherzadeh, “Stock price prediction using Deep learning algorithm and its comparison with machine learning algorithms,” Intelligent Systems in Accounting, Finance and Management, vol. 26, no. 4, pp. 164–174, Oct. 2019, doi: 10.1002/isaf.1459.

C. Y. Lai, R.-C. Chen, and R. E. Caraka, “Prediction Stock Price Based on Different Index Factors Using LSTM,” in 2019 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1–6, 2019, doi: 10.1109/ICMLC48188.2019.8949162.

T. Boechel, L. M. Policarpo, G. de O. Ramos, R. da Rosa Righi, and D. Singh, “Prediction of Harvest Time of Apple Trees: An RNN-Based Approach,” Algorithms, vol. 15, no. 3, p. 95, Mar. 2022, doi: 10.3390/a15030095.

A. Testas, “Decision Tree Regression with Pandas, Scikit-Learn, and PySpark,” in Distributed Machine Learning with PySpark, pp. 75–113, 2023, doi: 10.1007/978-1-4842-9751-3_4.

Y. Yu, K. Adu, N. Tashi, P. Anokye, X. Wang, and M. A. Ayidzoe, “RMAF: Relu-Memristor-Like Activation Function for Deep Learning,” IEEE Access, vol. 8, pp. 72727–72741, 2020, doi: 10.1109/ACCESS.2020.2987829.

S. R. Dubey, S. K. Singh, and B. B. Chaudhuri, “Activation functions in deep learning: A comprehensive survey and benchmark,” Neurocomputing, vol. 503, pp. 92–108, Sep. 2022, doi: 10.1016/j.neucom.2022.06.111.

S.-L. Shen, N. Zhang, A. Zhou, and Z.-Y. Yin, “Enhancement of neural networks with an alternative activation function tanhLU,” Expert Syst Appl, vol. 199, p. 117181, Aug. 2022, doi: 10.1016/j.eswa.2022.117181.

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, doi: 10.26555/jiteki.v9i4.27712.

A. Pranolo, X. Zhou, Y. Mao, and B. Widi, “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.

F. F. Rahani and P. A. Rosyady, “Quadrotor Altitude Control using Recurrent Neural Network PID,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 2, pp. 279–290, 2023, doi: 10.12928/biste.v5i2.8455.

M. F. Maulana, S. Sa’adah, and P. Eko Yunanto, “Crude Oil Price Forecasting Using Long Short-Term Memory,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 7, no. 2, p. 286, 2021, doi: 10.26555/jiteki.v7i2.21086.

Z. Chang, Y. Zhang, and W. Chen, “Effective Adam-Optimized LSTM Neural Network for Electricity Price Forecasting,” in 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), pp. 245–248, 2018, doi: 10.1109/ICSESS.2018.8663710.

K. Yudhaprawira Halim, D. Turianto Nugrahadi, M. Reza 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, doi: 10.26555/jiteki.v9i3.26354.

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, doi: 10.26555/jiteki.v9i3.26395.

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, doi: 10.26555/jiteki.v9i1.25765.

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, p. 199, Nov. 2023, doi: 10.17977/um018v6i22023p199-214.

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, doi: 10.26555/jiteki.v9i1.25375.

G. Sonkavde, D. S. Dharrao, A. M. Bongale, S. T. Deokate, D. Doreswamy, and S. K. Bhat, “Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications,” International Journal of Financial Studies, vol. 11, no. 3, p. 94, Jul. 2023, doi: 10.3390/ijfs11030094.

T. B. Shahi, A. Shrestha, A. Neupane, and W. Guo, “Stock Price Forecasting with Deep Learning: A Comparative Study,” Mathematics, vol. 8, no. 9, p. 1441, Aug. 2020, doi: 10.3390/math8091441.

S. Mirzaei, J.-L. Kang, and K.-Y. Chu, “A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization,” J Taiwan Inst Chem Eng, vol. 130, p. 104028, Jan. 2022, doi: 10.1016/j.jtice.2021.08.016.

K. Khalil, B. Dey, A. Kumar, and M. Bayoumi, “A Reversible-Logic Based Architecture for Long Short-Term Memory (LSTM) Network,” in 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5, 2021, doi: 10.1109/ISCAS51556.2021.9401395.

M. Moradi A., S. A. Sadrossadat, and V. Derhami, “Long Short-Term Memory Neural Networks for Modeling Nonlinear Electronic Components,” IEEE Trans Compon Packaging Manuf Technol, vol. 11, no. 5, pp. 840–847, May 2021, doi: 10.1109/TCPMT.2021.3071351.

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, p. 85, 2020, doi: 10.26555/jiteki.v5i2.15021.

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, p. 53, Jun. 2022, doi: 10.17977/um018v5i12022p53-66.

G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artif Intell Rev, vol. 53, no. 8, pp. 5929–5955, Dec. 2020, doi: 10.1007/s10462-020-09838-1.

J. Duan, P.-F. Zhang, R. Qiu, and Z. Huang, “Long short-term enhanced memory for sequential recommendation,” World Wide Web, vol. 26, no. 2, pp. 561–583, Mar. 2023, doi: 10.1007/s11280-022-01056-9.

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, doi: 10.12928/biste.v6i1.10120.

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, doi: 10.26555/jiteki.v9i4.28146.

K. A. Althelaya, E.-S. M. El-Alfy, and S. Mohammed, “Evaluation of bidirectional LSTM for short-and long-term stock market prediction,” in 2018 9th International Conference on Information and Communication Systems (ICICS), pp. 151–156, 2018, doi: 10.1109/IACS.2018.8355458.

W. Wei, H. Wu, and H. Ma, “An AutoEncoder and LSTM-Based Traffic Flow Prediction Method,” Sensors, vol. 19, no. 13, p. 2946, Jul. 2019, doi: 10.3390/s19132946.

M. Ma, C. Liu, R. Wei, B. Liang, and J. Dai, “Predicting machine’s performance record using the stacked long short‐term memory (LSTM) neural networks,” J Appl Clin Med Phys, vol. 23, no. 3, Mar. 2022, doi: 10.1002/acm2.13558.

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, doi: 10.26555/jiteki.v9i2.26222.

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, doi: 10.26555/jiteki.v9i1.25608.

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, p. 154, 2024, doi: 10.26555/jiteki.v10i1.28352.

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, doi: 10.26555/jiteki.v9i4.27014.

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, doi: 10.12928/biste.v6i1.10234.

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, doi: 10.12928/biste.v6i2.10785.

M. Jameaba, “Digitalization, Emerging Technologies, and Financial Stability: Challenges and Opportunities for the Indonesian Banking Sector and Beyond,” SSRN Electronic Journal, 2024, doi: 10.2139/ssrn.4808469.

M. Jameaba, “Digitization revolution, FinTech disruption, and financial stability: Using the case of Indonesian banking ecosystem to highlight wide-ranging digitization opportunities and major challenges,” SSRN Electronic Journal, 2020, doi: 10.2139/ssrn.3529924.

N. P. W. Rahayu, S. Bangsawan, Mahrinasari, and A. Ahadiat, “Service Quality, Employee Ethics, Bank Customer Trust: The Role of Size Moderation and Bank Reputation in Indonesia,” International Journal of Pharmaceutical Research, vol. 13, no. 2, Jul. 2021, doi: 10.31838/ijpr/2021.13.02.522.

M. Kautsar, E. E. Merawaty, S. Bahri, and A. H. Sutawidjaya, “Corporate Strategy Analysis in Increasing the Value of the Firm through Mergers and Acquisitions in the Banking Sector (Case Study on the BCA Group),” International Journal of Economics, Business and Management Research, vol. 7, no. 1, pp. 01–09, 2023, doi: 10.51505/IJEBMR.2023.7101.

A. E. Parung, L. S. Oppusunggu, and T. Sunaryo, “The Role of Risk Management in Competitiveness at PT Bank Central Asia TBK with an ERM (Enterprise Risk Management) Approach using Likelihood Table,” Journal of Economics, Finance and Management Studies, vol. 6, no. 12, Dec. 2023, doi: 10.47191/jefms/v6-i12-01.

S. M. N. Siahaan, I. Sadalia, and A. S. Silalahi, “Effect of Financial Ratios on Stock Returns with Earning Per Share as Moderating Variable in Banking Companies on the Indonesia Stock Exchange (2012-2017 Period),” International Journal of Research and Review, vol. 8, no. 8, pp. 398–406, Aug. 2021, doi: 10.52403/ijrr.20210855.

M. L. Widyanto, “Comparative Analysis of PT Bank Central Asia Tbk Performance before and after the COVID 19 Pandemic,” East African Scholars Journal of Economics, Business and Management, vol. 5, no. 1, pp. 16–21, Jan. 2022, doi: 10.36349/easjebm.2022.v05i01.003.

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, p. 188, Oct. 2023, doi: 10.17977/um018v6i22023p188-198.

H. Aini and H. Haviluddin, “Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach,” Knowledge Engineering and Data Science, vol. 2, no. 1, p. 1, Jun. 2019, doi: 10.17977/um018v2i12019p1-9.

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, p. 47, Dec. 2019, doi: 10.17977/um018v2i22019p47-57.

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, p. 14, Aug. 2021, doi: 10.17977/um018v4i12021p14-28.

K. K. Agustiningsih, E. Utami, and O. M. A. Alsyaibani, “Sentiment Analysis and Topic Modelling of The COVID-19 Vaccine in Indonesia on Twitter Social Media Using Word Embedding,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 8, no. 1, p. 64, 2022, doi: 10.26555/jiteki.v8i1.23009.

A. P. Wibawa et al., “Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting,” Knowledge Engineering and Data Science, vol. 6, no. 2, p. 170, Oct. 2023, doi: 10.17977/um018v6i22023p170-187.

Y. Sujatna et al., “Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks,” Knowledge Engineering and Data Science, vol. 6, no. 2, p. 215, 2023, doi: 10.17977/um018v6i22023p215-250.




DOI: https://doi.org/10.18196/jrc.v5i5.22460

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