Prediksi Harga Saham Batubara Menggunakan Recurrent Neural Network (RNN)
DOI:
https://doi.org/10.29407/tps0k354Abstract
Pergerakan harga saham dipengaruhi oleh berbagai faktor kompleks, termasuk kondisi makroekonomi, kebijakan politik, sentimen pasar, dan kinerja fundamental perusahaan. Tingginya volatilitas dan ketidakpastian yang terjadi di pasar modal Indonesia menuntut investor untuk menggunakan pendekatan prediksi yang andal guna mengoptimalkan strategi investasi. Dalam penelitian ini, dikembangkan model prediksi harga saham menggunakan metode Recurrent Neural Network (RNN), yang secara khusus dirancang untuk menganalisis data deret waktu dan mengenali pola temporal pada data historis harga saham. Data diperoleh dari platform Yahoo Finance dan melalui tahapan preprocessing seperti pembersihan data dan normalisasi agar sesuai dengan kebutuhan pemodelan. Hasil pengujian model RNN menunjukkan performa yang cukup baik dengan MAE sebesar 381.061, MSE 29.595.055, RMSE 544.013, dan MAPE 18,079%. Hasil ini menunjukkan bahwa RNN mampu mengikuti pola pergerakan harga saham secara akurat dan dapat dimanfaatkan sebagai alat bantu prediksi dalam mendukung keputusan investasi, khususnya di sektor energi.
Keywords:
data time series, deep learning, prediksi saham, RNN##plugins.themes.default.displayStats.downloads##
References
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.
S. D. Nabella, A. Munandar, and R. Tanjung, “Likuiditas, solvabilitas, aktivitas dan profitabilitas terhadap harga saham pada perusahaan sektor tambangan batu bara yang terdaftar di Bursa Efek Indonesia,” Measurement Jurnal Akuntansi, vol. 16, no. 1, pp. 97–102, 2022.
I. Yunianto, T. Haryanto, M. M. Mutoffar, Y. Fadhillah, and N. E. Putria, “Comparison of Stock Price Predictions Using Support Vector Regression and Recurrent Neural Network Methods,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), IEEE, 2023, pp. 927–932.
P. A. Riyantoko, T. M. Fahruddin, K. M. Hindrayani, and E. M. Safitri, “Analisis Prediksi Harga Saham Sektor Perbankan Menggunakan Algoritma Long-Short Terms Memory (Lstm),” in Seminar Nasional Informatika (SEMNASIF), 2020, pp. 427–435.
S. Samsudin, A. M. Harahap, and S. Fitrie, “Implementasi Gated Recurrent Unit (Gru) untuk Prediksi Harga Saham Bank Konvensional di Indonesia,” JISTech (Journal of Islamic Science and Technology), vol. 6, no. 2, 2021.
A. Arfan and E. T. P. Lussiana, “Perbandingan algoritma long short-term memory dengan SVR pada prediksi harga saham di Indonesia,” 2020.
N. K. Khairunisa and P. Hendikawati, “Long Short-Term Memory and Gated Recurrent Unit Modeling for Stock Price Forecasting,” Jurnal Matematika, Statistika dan Komputasi, vol. 21, no. 1, pp. 321–333, 2024.
S. J. Pipin, R. Purba, and H. Kurniawan, “Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation,” J. Comput. Syst. Informatics, vol. 4, no. 4, pp. 806–815, 2023.
K. Chahuán-Jiménez, “Neural Network-Based Predictive Models for Stock Market Index Forecasting,” Journal of Risk and Financial Management, vol. 17, no. 6, p. 242, 2024.
M. A. D. Suyudi, E. C. Djamal, and A. Maspupah, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2019.
A. Nilsen, “Perbandingan model rnn, model lstm, dan model gru dalam memprediksi harga saham-saham lq45,” Jurnal Statistika dan Aplikasinya, vol. 6, no. 1, pp. 137–147, 2022.
K. Kwanda, D. E. Herwindiati, and M. D. Lauro, “Perbandingan LSTM dan Bidirectional LSTM pada Sistem Prediksi Harga Saham Berbasis Website,” Ranah Research: Journal of Multidisciplinary Research and Development, vol. 7, no. 1, pp. 26–35, 2024.
R. A. A. Viadinugroho and D. Rosadi, “Long short-term memory neural network model for time series forecasting: case study of forecasting IHSG during Covid-19 outbreak,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 012016.
S. Samsudin, A. M. Harahap, and S. Fitrie, “Implementasi Gated Recurrent Unit (Gru) untuk Prediksi Harga Saham Bank Konvensional di Indonesia,” JISTech (Journal of Islamic Science and Technology), vol. 6, no. 2, 2021.
A. Arfan and E. T. P. Lussiana, “Perbandingan algoritma long short-term memory dengan SVR pada prediksi harga saham di Indonesia,” 2020.
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