STOCK PRICE PREDICTION USING THE LONG SHORT-TERM MEMORY METHOD
Abstract
Stocks are a highly risky investment instrument if not handled correctly. Therefore, accurately predicting stock prices is crucial to supporting better investment decisions. Today, more young people in the current generation know the importance of investing in stocks. Hence, understanding prediction methods early on is essential to reduce potential losses for prospective investors. With accurate prediction methods, the results will be more reliable. The data used consists of daily stock prices of Bank Syariah Indonesia from May 2019 to May 2024, totaling 1,215 data points. The research method employs LSTM (Long Short-Term Memory), which includes data collection, preprocessing, LSTM model formation, and model evaluation. The LSTM model is implemented using the Python programming language, and model evaluation is conducted using the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) metrics. The results show that the LSTM model can provide accurate predictions with a MAPE error value of only 1.72% and an RMSE of 53.49. This research indicates that the LSTM method is effective in predicting stock prices with an accuracy level of 98.28% and can be one of the bases when starting stock investment.
Downloads
References
R Citra, Puspa, and Abdul Ghoni. "Peranan Pasar Modal Dalam Perekonomian Negara Indonesia Muhammad. Jurnal AkunStie JAS 5 No. 2, 2019.
CNBC Indonesia. "Investor Gen-Z Dominasi Pasar Modal, Komposisi Nyaris 60%". Diakses dari https://www.cnbcindonesia.com/investasi/20230628120000-21-456789/apalagi-di-dunia-investasi-pasar-modal-indonesia pada 30 Mei 2023.
N. E. Monika and M. W. Yusniar, “Analisis Teknikal Menggunakan Indikator MACD dan RSI pada Saham JII,” Jurnal Riset Inspirasi Manajemen dan Kewirausahaan, vol. 4, no. 1, 2020, doi: 10.35130/jrimk.
F. S. Hidayat, G. Wiyono, and R. Kusumawardhani, “Pengaruh Faktor-Faktor Fundamental dan Teknikal terhadap Harga Saham Industri Manufaktur,” Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah, vol. 5, no. 6, pp. 3039–3057, May 2023, doi: 10.47467/alkharaj.v5i6.2707
Muhammad Sadikin and R. Agustina, “Analisis Fundamental dan Teknikal Saham BCA dan BRI (Tahun 2019-2021)”, SeNAPaN, vol. 3, no. 1, pp. 57–67, Oct. 2023.
Frecky, Frecky. "Analisis Pengaruh Faktor-Faktor Yang Mempengaruhi Harga Saham Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia. Jurnal Benefita 4 No. 1, 2019.
A. A. Ningrum et al., “Algoritma Deep Learning-Lstm Untuk Memprediksi Umur Transformator,” vol. 8, no. 3, pp. 539–548, 2021, doi: 10.25126/jtiik.202184587.
Suryani T., Adi W. Model LSTM untuk Prediksi Harga Saham pada Pasar Saham Indonesia. Jurnal Manajemen Teknologi. 2022.
E. L. Utama and F. S. Riyadi, "Penerapan Metode ARIMA dan ANN untuk Prediksi Harga Saham pada Perusahaan XYZ," Jurnal Manajemen dan Kewirausahaan, vol. 6, no. 4, pp. 123-130, Dec. 2021.
F. P. Kurniawan, "Implementasi Algoritma Genetika dan ANN untuk Prediksi Saham," Jurnal Teknologi dan Sistem Informasi, vol. 8, no. 2, pp. 155-162, Jun. 2022.
M. R. Pahlawan, A. Djunaidy, and R. A. Vinarti, "Prediksi Indeks Harga Saham Menggunakan Model Hibrida Recurrent Neural Network Dan Genetic Algorithm," ITS Journal, vol. 8, no. 2, pp. 123-135, 2022.
R. M. Simanullang, R. Sembel, posma S. J. Kennedy, R. Lumbantoruan, and S. J. Tobing, “Pengaruh Indeks Harga Saham Global terhadap Indeks Harga Saham Indonesia Studi Kasus Sebelum dan Selama Periode Covid- 19”, IKRAITH-EKONOMIKA, vol. 6, no. 2, pp. 143 - 152, Oct. 2022.
Iqbal, M., & Ningsih, N. W. Prediksi Harga Saham Harian PT BTPN Syariah Tbk Menggunakan Model Arima dan Model Garch. Jurnal Ilmiah Ekonomi Islam, 7(3), 1573–1580, 2021.
C. Kirana, P. #1, S. Fachri, P. #2, R. Nuraini, and S. Fatonah, “Meningkatkan Akurasi Long-Short Term Memory (LSTM) pada Analisis Sentimen Vaksin Covid-19 di Twitter dengan Glove,” Jurnal Telematika, vol. 16, no. 2, 2021 [Online]. Available: https://t.co/7IM3eFsBSU
Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, and Antika Zahrotul Kamalia, “Perbandingan Algoritma Linear Regression, Lstm, Dan Gru Dalam Memprediksi Harga Saham Dengan Model Time Series,” SEMINASTIKA, vol. 3, no. 1, pp. 39–46, Nov. 2021, doi: 10.47002/seminastika.v3i1.275.
E. Ivan and H. D. Purnomo, “Forecasting Prices Of Fertilizer Raw Materials Using Long Short Term Memory,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 6, pp. 1663–1673, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.433.
E. Iin and Anita Muliawati. "Prediksi pergerakan harga saham pada sektor farmasi menggunakan algoritma long short-term memory. jurnal informatika, edisi ke-17, Nomor 2, Agustus 2021.
A. S. Lombu, I. Vitra Paputungan, and C. K. Dewa, “Predicting Fantasy Premier League Points Using Convolutional Neural Network And Long Short Term Memory,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 1, pp. 263–272, 2024, doi: 10.52436/1.jutif.2024.5.1.1792.
W. W. K. Wardani, “Prediksi Harga Saham Syariah menggunakan Metode Recurrent Neural Network-Long Short Term Memory,” UIN Sunan Ampel Surabaya, 2021. [Online]. Available: http://digilib.uinsa.ac.id/49542.
R. Irwan et al., “Implementation Of Lstm (Long Short Term Memory) Algorithm To Predict Weather In Central Java,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 6, pp. 1347–1357, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1118.
E. S. Nugraha, Z. Alika, and D. Amir Hamzah, “Forecasting the Stock Price of PT Astra International Using the LSTM Method”, J. RESTI (Rekayasa Sist. Teknol. Inf.) , vol. 8, no. 3, pp. 431 - 437, Jun. 2024.
Akil and I. Chaidir, "Prediksi Harga Saham Twitter Dengan Long Short-Term Memory Recurrent Neural Network," INTI Nusa Mandiri, vol. 17, no. 1, pp. 1-7, Aug. 2022.
Qotrunnada, F. Implementasi Long Short Term Memory pada Optimalisasi Prediksi Harga Saham Menggunakan Parameter Analisis Teknikal. (Tesis, Universitas Islam Negeri Maulana Malik Ibrahim Malang), 2023.
Copyright (c) 2024 Muhammad Sahroni, Mochammad Firman Arif, Muhammad Misdram
This work is licensed under a Creative Commons Attribution 4.0 International License.
