Prediksi Harga Saham (FREN.JK) PT. Smartfren Telecom Tbk. Menggunakan Algoritma Long Short Term Memory
Kata Kunci:
Prediksi, Saham, Long Short Term Memory, SpearmanAbstrak
Peningkatan minat masyarakat terhadap investasi saham memerlukan metode prediksi yang akurat untuk meminimalisir risiko kerugian. Pengembangan model prediksi harga saham PT. Smartfren Telecom Tbk (FREN.JK) menggunakan algoritma Long Short Term Memory (LSTM), yang merupakan pengembangan dari Recurrent Neural Network (RNN). Model dikembangkan menggunakan data harian harga penutupan saham periode 1 Januari 2023 hingga 31 Juli 2024 dari Yahoo Finance, dengan pembagian data 80% : 20% untuk training dan testing. Inovasi penelitian terletak pada integrasi variabel persediaan dan piutang usaha pihak berelasi sebagai fitur tambahan, yang dipilih berdasarkan analisis korelasi spearman. Hasil menunjukkan bahwa model LSTM dengan fitur terpilih menghasilkan nilai error yang lebih kecil (RMSE 14603, MAPE 21%) dibandingkan model dengan seluruh variabel aset lancar (RMSE 15526, MAPE 23%). Penelitian ini berkontribusi pada pengembangan model prediksi harga saham yang mengintegrasikan variabel keuangan relevan dengan algoritma LSTM.
Referensi
Adlini, M. N., Dinda, A. H., Yulinda, S., Chotimah, O., & Merliyana, S. J. (2022). Metode Penelitian Kualitatif Studi Pustaka. Edumaspul: Jurnal Pendidikan, 6(1), 974–980. https://doi.org/10.33487/edumaspul.v6i1.3394
Aldi, M. W. P., Jondri, & Aditsania, A. (2018). Analisis dan Implementasi Long Short Term Memory Neural Network Untuk Prediksi Harga Bitcoin. E-Proceeding of Engineering, 5(2), 3548–3555.
Azmi, U., Atok, R. M., Syaifudin, W. H., Siswono, G. O., Ahmad, I. S., & Wahyuningsih, N. (2023). Proyeksi Tingkat Kematian di Indonesia Menggunakan Metode Holt-Winters Smoothing Exponential dan Moving Average. Limits: Journal of Mathematics and Its Applications, 20(1), 25. https://doi.org/10.12962/limits.v20i1.8132
Chatfield. (2005). Time-series forecasting. 131–133.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623
de Amorim, L. B. V., Cavalcanti, G. D. C., & Cruz, R. M. O. (2022). The choice of scaling technique matters for classification performance. Applied Soft Computing, 133, 1–37. https://doi.org/10.1016/j.asoc.2022.109924
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Hanifa, A., Fauzan, S. A., Hikal, M., & Ashfiya, M. B. (2021). Perbandingan Metode LSTM dan GRU (RNN) untuk Klasifikasi Berita Palsu Berbahasa Indonesia. Dinamika Rekayasa, 17(1), 33. https://doi.org/10.20884/1.dr.2021.17.1.436
Kafil, M. (2019). Penerapan Metode K-Nearest Neighbors Untuk Prediksi Penjualan Berbasis Web Pada Boutiq Dealove Bondowoso. JATI (Jurnal Mahasiswa Teknik Informatika), 3(2), 59–66. https://doi.org/10.36040/jati.v3i2.860
Kuber, V., Yadav, D., & Yadav, A. K. (2022). Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction. http://arxiv.org/abs/2205.06673
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197(February), 116659. https://doi.org/10.1016/j.eswa.2022.116659
Mehtab, S., Sen, J., & Dutta, A. (2021). Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models. Communications in Computer and Information Science, 1366, 88–106. https://doi.org/10.1007/978-981-16-0419-5_8
OJK. (2023). Buku Saku Pasar Modal. Djajadi, Inarno, 5. https://www.ojk.go.id/id/berita-dan-kegiatan/info-terkini/Documents/Pages/Buku-Saku-Pasar-Modal/BUKU SAKU PSR MODAL OJK 2023.pdf
Sen, J., Mehtab, S., & Dutta, A. (2021). Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH. 2021 Asian Conference on Innovation in Technology, ASIANCON 2021. https://doi.org/10.1109/ASIANCON51346.2021.9544977
Tilasefana, R. A., & Putra, R. E. (2023). Penerapan Metode Deep Learning Menggunakan Algoritma CNN Dengan Arsitektur VGG NET Untuk Pengenalan Cuaca. Journal of Informatics and Computer Science (JINACS), 05(1), 48–57.
Adlini, M. N., Dinda, A. H., Yulinda, S., Chotimah, O., & Merliyana, S. J. (2022). Metode Penelitian Kualitatif Studi Pustaka. Edumaspul: Jurnal Pendidikan, 6(1), 974–980. https://doi.org/10.33487/edumaspul.v6i1.3394
Aldi, M. W. P., Jondri, & Aditsania, A. (2018). Analisis dan Implementasi Long Short Term Memory Neural Network Untuk Prediksi Harga Bitcoin. E-Proceeding of Engineering, 5(2), 3548–3555.
Azmi, U., Atok, R. M., Syaifudin, W. H., Siswono, G. O., Ahmad, I. S., & Wahyuningsih, N. (2023). Proyeksi Tingkat Kematian di Indonesia Menggunakan Metode Holt-Winters Smoothing Exponential dan Moving Average. Limits: Journal of Mathematics and Its Applications, 20(1), 25. https://doi.org/10.12962/limits.v20i1.8132
Chatfield. (2005). Time-series forecasting. 131–133.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623
de Amorim, L. B. V., Cavalcanti, G. D. C., & Cruz, R. M. O. (2022). The choice of scaling technique matters for classification performance. Applied Soft Computing, 133, 1–37. https://doi.org/10.1016/j.asoc.2022.109924
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Hanifa, A., Fauzan, S. A., Hikal, M., & Ashfiya, M. B. (2021). Perbandingan Metode LSTM dan GRU (RNN) untuk Klasifikasi Berita Palsu Berbahasa Indonesia. Dinamika Rekayasa, 17(1), 33. https://doi.org/10.20884/1.dr.2021.17.1.436
Kafil, M. (2019). Penerapan Metode K-Nearest Neighbors Untuk Prediksi Penjualan Berbasis Web Pada Boutiq Dealove Bondowoso. JATI (Jurnal Mahasiswa Teknik Informatika), 3(2), 59–66. https://doi.org/10.36040/jati.v3i2.860
Kuber, V., Yadav, D., & Yadav, A. K. (2022). Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction. http://arxiv.org/abs/2205.06673
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197(February), 116659. https://doi.org/10.1016/j.eswa.2022.116659
Mehtab, S., Sen, J., & Dutta, A. (2021). Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models. Communications in Computer and Information Science, 1366, 88–106. https://doi.org/10.1007/978-981-16-0419-5_8
OJK. (2023). Buku Saku Pasar Modal. Djajadi, Inarno, 5. https://www.ojk.go.id/id/berita-dan-kegiatan/info-terkini/Documents/Pages/Buku-Saku-Pasar-Modal/BUKU SAKU PSR MODAL OJK 2023.pdf
Sen, J., Mehtab, S., & Dutta, A. (2021). Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH. 2021 Asian Conference on Innovation in Technology, ASIANCON 2021. https://doi.org/10.1109/ASIANCON51346.2021.9544977
Tilasefana, R. A., & Putra, R. E. (2023). Penerapan Metode Deep Learning Menggunakan Algoritma CNN Dengan Arsitektur VGG NET Untuk Pengenalan Cuaca. Journal of Informatics and Computer Science (JINACS), 05(1), 48–57.
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