Metode Hybrid SVR-GWO Untuk Prediksi Harga Saham PT. Aneka Tambang Tbk
Abstract
Fluctuating and unpredictable stock price movements pose a challenge for investors in their decision-making. This study aims to apply and analyze the performance of a hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) model in predicting the stock price of PT Aneka Tambang Tbk. The data used consists of daily stock prices from September 11, 2020, to September 11, 2025, totaling 1,202 data points, with a division of 70% training data and 30% testing data. The research stages include pre-processing, basic SVR modeling, and parameter optimization using GWO. The evaluation was carried out using RMSE, MAE, and MAPE. The results show that GWO optimization improved the model's performance from RMSE 99.78, MAE 55.70, and MAPE 2.61% to RMSE 77.27, MAE 48.97, and MAPE 2.37%. Thus, the SVR–GWO model is capable of improving the accuracy of stock price predictions and has the potential to support investment decision-making.
Keyword: Grey Wolf Optimizer; Machine Learning; Prediction; Stock Price; Support Vector Re-gression
Abstrak
Pergerakan harga saham yang fluktuatif dan sulit diprediksi menjadi tantangan bagi investor dalam pengambilan keputusan. Penelitian ini bertujuan menerapkan dan menganalisis kinerja model hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) dalam memprediksi harga saham PT Aneka Tambang Tbk. Data yang digunakan berupa harga saham harian periode 11 September 2020 hingga 11 September 2025 sebanyak 1202 data, dengan pembagian 70% data pelatihan dan 30% data pengujian. Tahapan penelitian meliputi pre-processing, pemodelan SVR dasar, serta optimasi parameter menggunakan GWO. Evaluasi dilakukan menggunakan RMSE, MAE, dan MAPE. Hasil menunjukkan bahwa optimasi GWO meningkatkan kinerja model dari RMSE 99.78, MAE 55.70, dan MAPE 2.61% menjadi RMSE 77.27, MAE 48.97, dan MAPE 2.37%. Dengan demikian, model SVR–GWO mampu meningkatkan akurasi prediksi harga saham dan berpotensi mendukung pengambilan keputusan investasi.
Kata Kunci: Grey Wolf Optimizer; Harga Saham; Machine Learning; Prediksi; Support Vector Regression
References
I. Sukartaatmadja, S. Khim, and M. N. Lestari, “Faktor-faktor Yang Mempengaruhi Harga Saham Perusahaan,” J. Ilm. Manaj. Kesatuan, vol. 11, no. 1, pp. 21–40, 2023, doi: 10.37641/jimkes.v11i1.1627.
Amelya Rosalina Siringoringo et al., “Analisis Pengaruh BI Rate dan Inflasi Terhadap Indeks Harga Saham Gabungan Di Indonesia Periode 2019-2023: Studi Dengan Model VAR,” EKOMA J. Ekon. Manajemen, Akunt., vol. 4, no. 3, pp. 5483–5490, 2025, doi: 10.56799/ekoma.v4i3.7227.
J. Bao and T. Morimoto, “Machine Learning with Applications High-frequency stock price prediction via deep learning,” Mach. Learn. with Appl., vol. 21, no. April, p. 100716, 2025, doi: 10.1016/j.mlwa.2025.100716.
F. Nur Fajri, A. Tholib, and W. Yuliana, “Application of Machine Learning Algorithm for Determining Elective Courses in Informatics Study Program,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 3, pp. 485–496, 2022, doi: 10.28932/jutisi.v8i3.3990.
L. Gao, Y. Shangguan, Z. Sun, Q. Shen, and Z. Shi, “Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning,” Remote Sens., vol. 16, no. 3, pp. 1–19, 2024, doi: 10.3390/rs16030514.
A. K. Yadav and V. P. Vishwakarma, “An Integrated Blockchain Based Real Time Stock Price Prediction Model by CNN, Bi LSTM and AM,” Procedia Comput. Sci., vol. 235, no. 2023, pp. 2630–2640, 2024, doi: 10.1016/j.procs.2024.04.248.
D. S. Metwally, M. Ali, S. M. Alghamdi, and D. M. Khan, “A novel hybrid model to forecast the stock price based on CEEMDAN and support vector regression,” J. Radiat. Res. Appl. Sci., vol. 18, no. 2, p. 101385, 2025, doi: 10.1016/j.jrras.2025.101385.
S. A. Amellia Kharis, A. H. Anna Zili, M. Malik, W. Nuryaningrum, and A. Putri, “Comparing machine learning models for Indonesia stock market prediction,” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 1, p. 508, 2025, doi: 10.11591/ijeecs.v38.i1.pp508-516.
P. Gupta, S. K. Gupta, and R. S. Jadon, “Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 11s, pp. 309–318, 2023, doi: 10.17762/ijritcc.v11i11s.8103.
Z. Sun, F. Liu, Y. Han, and R. Min, “Prediction of ultimate bearing capacity of rock-socketed piles based on GWO-SVR algorithm,” Structures, vol. 61, no. January, p. 106039, 2024, doi: 10.1016/j.istruc.2024.106039.
S. Shafiee, L. M. Lied, I. Burud, J. A. Dieseth, M. Alsheikh, and M. Lillemo, “Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery,” Comput. Electron. Agric., vol. 183, no. 1432, p. 106036, 2021, doi: 10.1016/j.compag.2021.106036.
Y. Sun, S. Mutalib, Omar, and Peng, “Research on Stock Prediction Model using Mode Decomposition Algorithm and Support Vector Regression,” in Proceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, New York, NY, USA: ACM, Jan. 2024, pp. 378–384. doi: 10.1145/3675417.3675480.
I. F. Riziq and A. R. Dzikrillah, “Impelementasi Algoritma LSTM Dan SVR Untuk Prediksi Harga Bitcoin Menggunakan Data Yahoo Finance,” pp. 284–291, 2025, doi: 10.47002/metik.v9i2.1077.
J. Ma et al., “Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study,” Landslides, vol. 19, no. 10, pp. 2489–2511, 2022, doi: 10.1007/s10346-022-01923-6.
İ. Tuğal, “Energy efficiency in building: Entropy-based Grey Wolf Optimization for improved MLP performance,” Energy Reports, vol. 13, no. December 2024, pp. 4247–4260, 2025, doi: 10.1016/j.egyr.2025.03.048.
M. Munawaroh, T. Azhima, Y. Siswa, and W. J. Pranoto, “Optimasi Metode BMR dan Simulated Annealing untuk Algoritma SVM dalam Mengatasi Imbalanced dan High Dimensional Data Stunting,” vol. 05, no. 3, pp. 1–19, 2024, [Online]. Available: https://ijurnal.com/1/index.php/jpip
Siti Muawwanah, T. A. Y. Siswa, and Wawan Joko Pranoto, “Model Optimasi SVM-GSBE dalam Menangani High Dimensional Data Stunting Kota Samarinda,” J. Teknol. Sist. Inf. dan Apl., vol. 7, no. 3, pp. 1246–1258, 2024, doi: 10.32493/jtsi.v7i3.41545.
O. Surakhi et al., “Time-lag selection for time-series forecasting using neural network and heuristic algorithm,” Electron., vol. 10, no. 20, pp. 1–22, 2021, doi: 10.3390/electronics10202518.
F. Putra, H. F. Tahiyat, R. M. Ihsan, Rahmaddeni, and L. Efrizoni, “Application of K-Nearest Neighbor Algorithm Using Wrapper as Preprocessing for Determination of Human Weight Information,” 2024 IEEE 5th Int. Conf. Converg. Technol. I2CT 2024, vol. 4, no. January, pp. 273–281, 2024, doi: 10.1109/I2CT45611.2019.9033691.
A. Widyanto, K. Kusrini, and K. Kusnawi, “Pengaruh Keseimbangan Data terhadap Akurasi Model Support Vector Machine pada Data Set Donor Darah,” J. Teknol. Terpadu, vol. 9, no. 2, pp. 79–88, 2023, doi: 10.54914/jtt.v9i2.771.
N. M. Aruan, G. W. Simanjuntak, and A. I. Siagian, “Pendekatan Algoritma Support Vector Regression Dalam Memprediksi Harga Cryptocurrency (Studi Kasus: Binance),” J. Tek. Inform. dan Sist. Inf., vol. 10, no. 3, pp. 531–541, 2023, [Online]. Available: http://jurnal.mdp.ac.id
Z. Qi, Y. Feng, S. Wang, and C. Li, “Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques,” Ain Shams Eng. J., vol. 16, no. 1, p. 103206, 2025, doi: 10.1016/j.asej.2024.103206.
S. Sharma and I. Ali, “Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid,” e-Prime - Adv. Electr. Eng. Electron. Energy, vol. 10, no. 2023, p. 100804, Dec. 2024, doi: 10.1016/j.prime.2024.100804.
Z. Annisa, N. Azizah, I. Cholissodin, and L. Muflikhah, “Prediksi Hasil Panen Tanaman Biofarmaka di Indonesia dengan Menggunakan Metode Extreme Learning Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 11, pp. 5331–5338, 2022.
K. H. Suradiradja, “Algoritme Machine Learning Multi-Layer Perceptron dan Recurrent Neural Network untuk Prediksi Harga Cabai Merah Besar di Kota Tangerang,” Fakt. Exacta, vol. 14, no. 4, p. 194, 2022, doi: 10.30998/faktorexacta.v14i4.10376.
E. Ofori-Ntow Jnr, Y. Y. Ziggah, M. J. Rodrigues, and S. Relvas, “A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction,” Results Eng., vol. 14, no. December 2021, p. 100399, 2022, doi: 10.1016/j.rineng.2022.100399.
Z. G. Fang, S. Q. Yang, C. X. Lv, S. Y. An, and W. Wu, “Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study,” BMJ Open, vol. 12, no. 7, pp. 1–8, 2022, doi: 10.1136/bmjopen-2021-056685.
Y. Altork, “Comparative analysis of machine learning models for wind speed forecasting: Support vector machines, fine tree, and linear regression approaches,” Int. J. Thermofluids, vol. 27, no. April, p. 101217, 2025, doi: 10.1016/j.ijft.2025.101217.
Y. C. Dewi and J. Purwadi, “Optimasi Parameter Support Vector Regression (SVR) Menggunakan Algoritma Grey Wolf Optimizer (GWO),” J. Ilm. Mat., vol. 10, no. 1, pp. 25–33, Apr. 2025, doi: 10.26555/jim.v10i1.30867.
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