Aplikasi Forecasting Saham Bank Bni Berbasis Android
DOI:
https://doi.org/10.29407/vkp9k637Abstract
Latar belakang penelitian ini adalah fluktuasi harga saham BNI yang signifikan, serta kurangnya pengetahuan investor pemula dalam menentukan strategi investasi. Penelitian ini bertujuan untuk merancang dan membangun sistem prediksi harga saham Bank Negara Indonesia (BNI) berbasis Android menggunakan algoritma Long Short-Term Memory (LSTM). Data historis harga saham dikumpulkan melalui Yahoo Finance dan dinormalisasi menggunakan metode Min-Max Scaling. Model LSTM dibangun dan dioptimasi dengan teknik deep learning, kemudian diintegrasikan ke dalam aplikasi Android melalui backend Django. Hasil evaluasi performa model dilakukan menggunakan metrik Mean Absolute Error (MAE) adalah 0.075030, Mean Squared Error (MSE) adalah 0.008613, dan Root Mean Squared Error (RMSE) adalah 0.092806. Hasil penelitian menunjukkan bahwa model LSTM mampu memprediksi tren harga saham BNI secara efektif, sehingga dapat menjadi alat bantu bagi investor dalam mengambil keputusan investasi yang lebih informatif. Aplikasi yang dihasilkan juga memberikan visualisasi prediksi dalam bentuk grafik dan tabel, sehingga lebih mudah dipahami oleh pengguna.
Keywords:
Android, BNI, Forecasting, LSTM, Saham##plugins.themes.default.displayStats.downloads##
References
[1] A. Al-Ajlouni, “Financial technology in banking industry: Challenges and opportunities,” in e International Conference on Economics and Administrative Sciences ICEAS2018, 2018.
[2] N. van der Wijst, Discrete-Time Asset Pricing Models in Applied Stochastic Finance. Chichester, UK: Wiley, 2012.
[3] N. Metawa, M. K. Hassan, and S. Metawa, Artificial Intelligence and Big Data for Financial Risk Management. Routledge. https://doi. org/10.4324/9781003144410, 2022.
[4] E. M. Akimova, E. M. Stein, and Y. S. Prokhorova, “System analysis in the investment processes management and theoretical principles of the investments assessment,” Journal of Advanced Research in Law and Economics, vol. 6, no. 3, pp. 472–487, 2015.
[5] B. E. Indonesi, “Daftar Saham LQ45.”
[6] H. Suhendri, S. Indah, and A. H. Al-Abi Syahril, “Analisis Perubahan Harga Saham Bank Syariah BUMN sebelum Merger dan Pasca Merger,” Referensi: Jurnal Ilmu Manajemen dan Akuntansi, vol. 10, no. 2, pp. 84–90, 2022.
[7] S. E. Ady Inrawan, Buku Ajar Manajemen Keuangan. Zahir Publishing.
[8] R. C. Staudemeyer and E. R. Morris, “Understanding LSTM--a tutorial into long short-term memory recurrent neural networks,” arXiv preprint arXiv:1909.09586, 2019.
[9] J. Brownlee, Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery, 2018.
[10] A. Moghar and M. Hamiche, “Stock market prediction using LSTM recurrent neural network,” Procedia Comput Sci, vol. 170, pp. 1168–1173, 2020.
[11] M. Nirraca and E. Hartati, “Prediksi harga bitcoin menggunakan metode long short term memory”, doi: 10.32502/digital.v7i1.7974.
[12] A. Yusuf and B. K. Selatan, “PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN LONG SHORT-TERM MEMORY,” vol. 15, no. 2, pp. 124–132, 2021, [Online]. Available: http://ppjp.ulm.ac.id/journals/index.php/epsilon
[13] D. Chandranegara, R. Afif, C. Aditya, W. Suharso, and H. Wibowo, “Prediksi Harga Saham Jakarta Islamic Index Menggunakan Metode Long Short-Term Memory,” 2023.
[14] Moch Farryz Rizkilloh and Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 1, pp. 25–31, Feb. 2022, doi: 10.29207/resti.v6i1.3630.
[15] T. G. Lasijan, R. Santoso, and A. R. Hakim, “PREDIKSI HARGA EMAS DUNIA MENGGUNAKAN METODE LONG-SHORT TERM MEMORY,” Jurnal Gaussian, vol. 12, no. 2, pp. 287–295, Jul. 2023, doi: 10.14710/j.gauss.12.2.287-295.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Regi Cendika Pratama, Umi Mahdiyah, Resty Wulanningrum
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
