Analisis Perbandingan Model Jaringan Saraf Tiruan dan Support Vector Machine dalam Memprediksi Indeks Harga Saham Gabungan
DOI:
https://doi.org/10.31941/jurnalpena.v38i2.4942Keywords:
Neural Network, Support Vector Machine, Particle Swarm Optimization, Evolutionary Algorithm, Stock PredictionAbstract
The Jakarta Composite Index (IHSG) is a key indicator that reflects the performance of the stock market in Indonesia. It is often used by investors, analysts, and decision-makers to assess economic conditions and make investment decisions. However, the fluctuating and dynamic nature of the stock market makes predicting the IHSG a significant challenge. This study compares the effectiveness of Neural Network (NN) and Support Vector Machine (SVM) with optimization methods such as Particle Swarm Optimization (PSO) and Evolutionary Algorithm (EVO) in predicting stock prices. The results show that the combination of SVM with EVO provides the best prediction accuracy with the lowest error values (RMSE: 0.07, MAE: 0.09, MSE: 0.004). In contrast, NN with PSO and EVO showed higher prediction errors, indicating lower accuracy levels. These findings highlight the potential of optimization methods in enhancing the performance of stock prediction models, with SVM+EVO being the most effective combination.References
Alkhatib, K., Khazaleh, H., Alkhazaleh, H. A., Alsoud, A. R., & Abualigah, L. (2022). A New Stock Price Forecasting Method Using Active Deep Learning Approach. Journal of Open Innovation: Technology, Market, and Complexity, 8(2). https://doi.org/10.3390/joitmc8020096
Babanezhad, M., Behroyan, I., Nakhjiri, A. T., Marjani, A., Rezakazemi, M., & Shirazian, S. (2020). High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system. Scientific Reports, 10(1), 21304. https://doi.org/10.1038/s41598-020-78277-3
Bates, S., Hastie, T., & Tibshirani, R. (2024). Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of the American Statistical Association, 119(546), 1434–1445. https://doi.org/10.1080/01621459.2023.2197686
Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
Kandanaarachchi, S., Muñoz, M. A., Hyndman, R. J., & Smith-Miles, K. (2020). On normalization and algorithm selection for unsupervised outlier detection. Data Mining and Knowledge Discovery, 34(2), 309–354. https://doi.org/10.1007/s10618-019-00661-z
Khaira, U., Utomo, P. E. P., Suratno, T., & Gulo, P. C. S. (2021). Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Autoregressive Integrated Moving Average (ARIMA). JUSS (Jurnal Sains Dan Sistem Informasi), 2(2), 11–17. https://doi.org/10.22437/juss.v2i2.8449
Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, & Antika Zahrotul Kamalia. (2021). Stock Price Prediction using Time Series Model Comparing Linier Regression, LSTM and GRU Algoritm. Seminastika, 3(1), 39–46. https://doi.org/10.47002/seminastika.v3i1.275
Korade, N. B., & Zuber, M. (2022). Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques. International Journal of Advanced Computer Science and Applications, 13(11), 378–385. https://doi.org/10.14569/IJACSA.2022.0131142
Kumar Chandar, S. (2019). Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01224-2
Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2023). A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting. Annals of Data Science, 10(1), 183–208. https://doi.org/10.1007/s40745-021-00344-x
Lina Situngkir, T. (2019). Pengaruh Jangka Pendek Dan Jangka Panjang Variabel Suku Bunga Terhadap Indeks Harga Saham Gabungan (Ihsg) Dengan Pendekatan Error Correction Model. Sistemik : Jurnal Ilmiah Nasional Bidang Ilmu Teknik, 7(2), 28–33. https://doi.org/10.53580/sistemik.v7i2.26
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741–4753. https://doi.org/10.1007/s00521-020-05532-z
Midilli, Y. E., & Parshutin, S. (2019). Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction. Information Technology and Management Science, 22(December), 15–21. https://doi.org/10.7250/itms-2019-0003
Nayak, S. C., & Misra, B. B. (2019). A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction. Financial Innovation, 5(1), 1–34. https://doi.org/10.1186/s40854-019-0153-1
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00299-5
Rokhsatyazdi, E., Rahnamayan, S., Amirinia, H., & Ahmed, S. (2020). Optimizing LSTM Based Network For Forecasting Stock Market. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–7. https://doi.org/10.1109/CEC48606.2020.9185545
S, K. C. (2021). Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms. Pattern Recognition Letters, 147, 124–133. https://doi.org/https://doi.org/10.1016/j.patrec.2021.03.030
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0131-7
Trierweiler Ribeiro, G., Alves Portela Santos, A., Cocco Mariani, V., & dos Santos Coelho, L. (2021). Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility. Expert Systems with Applications, 184, 115490. https://doi.org/https://doi.org/10.1016/j.eswa.2021.115490
Vrigazova, B. (2021). The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems. Business Systems Research, 12(1), 228–242. https://doi.org/10.2478/bsrj-2021-0015
Zhu, J., Li, S., Song, J., & Wang, Y. (2021). Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network. Frontiers in Earth Science, 9. https://doi.org/10.3389/feart.2021.653226
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