Pengembangan Sistem Prediksi Harga Saham Berbasis Web Menggunakan Model LSTM dan CNN–LSTM
Abstract
This study discussed the challenges of stock price prediction, which is volatile and exhibits non-linear patterns, as well as the need for an easily accessible system to present prediction results quickly. A web-based application was developed to predict the closing prices of Indonesian non-banking stocks (ASII, TLKM, and UNVR) by utilizing daily market data that were updated periodically through an application programming interface. The integrated pre-trained models used were long short-term memory (LSTM) and convolutional neural network–long short-term memory (CNN-LSTM), which were integrated into the application for one-step-ahead (t+1) inference. The system development methodology followed the software development life cycle waterfall model. Functional testing using a black-box testing approach showed that the core features ran according to the requirements, so the application was considered suitable as a web-based medium for prediction and visualization.
Keywords: Stock price prediction; Web application; Deep learning; Application programming interface; Non-banking stocks
Abstrak
Pergerakan harga saham yang volatil dan non-linear menuntut pendekatan prediksi yang adaptif serta sistem yang mudah diakses. Penelitian ini bertujuan untuk mengembangkan suatu sistem prediksi harga penutupan saham dan menyajikannya secara cepat. Penelitian ini telah mengembangkan aplikasi berbasis web untuk prediksi harga penutupan saham emiten non-perbankan Indonesia (ASII, TLKM, dan UNVR) dengan memanfaatkan data pasar harian yang diperbarui berkala melalui application programming interface. Integrasi model terlatih yang digunakan yaitu long short-term memory (LSTM) dan convolutional neural network-long short-term memory (CNN-LSTM), yang diintegrasikan ke dalam aplikasi untuk proses inference satu langkah ke depan (t+1). Metodologi pengembangan sistem mengikuti software development life cycle model Waterfall. Sistem yang dikembangkan telah berfungsi sesuai dengan kebutuhan berdasarkan hasil black-box testing.
Keywords
References
K. R. Baskaran and B. Kaviya, “Stock Market Prediction Using Machine Learning and Deep Learning Algorithms,” Sustain. Digit. Technol. Smart Cities Heal. Commun. Transp., pp. 127–138, 2023, doi: 10.1201/9781003307716-12.
H. Nazhiroh, Dina Fitria, Dony Permana, and Zilrahmi, “PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM),” UNP J. Stat. Data Sci., vol. 2, no. 4, pp. 414–421, 2024, doi: 10.24036/ujsds/vol2-iss4/223.
V. Arinal and M. Puspita, “Peningkatan Akurasi Nilai Harga Saham Menggunakan Metode Long Short-Term Memory (LSTM) pada PT Unilever Tbk,” J. Indones. Manaj. Inform. dan Komun., vol. 6, no. 1, pp. 252–260, 2025, doi: 10.35870/jimik.v6i1.1190.
M. R. Luthfi and R. D. Syah, “Model Deep Learning Untuk Analisis Prediksi Harga Saham Menggunakan Metode Long Short Term Memory (Lstm),” J. Ilm. Ekon. Bisnis, vol. 30, no. 1, pp. 201–213, 2025, doi: 10.35760/eb.2025.v30i1.11870.
F. R. Pratama, B. Santoso, and S. Kacung, “Prediksi Harga Saham Pt Telkom Menggunakan Metode CNN-LSTM,” J. Inf. Syst. Manag., vol. 7, no. 1, pp. 66–70, 2025, doi: 10.24076/joism.2025v7i1.2087.
A. Y. Febriyanti, D. A. Prasetya, and T. Trimono, “Stock Price Prediction and Risk Estimation Using Hybrid CNN-LSTM and VaR-ECF,” J. Tek. Inform., vol. 6, no. 3, pp. 1539–1554, 2025, doi: 10.52436/1.jutif.2025.6.3.4648.
H. Hewamalage, K. Ackermann, and C. Bergmeir, “Forecast evaluation for data scientists: common pitfalls and best practices,” Data Min. Knowl. Discov., vol. 37, no. 2, pp. 788–832, 2023, doi: 10.1007/s10618-022-00894-5.
N. Beck, J. Dovern, and S. Vogl, “Mind the naive forecast ! a rigorous evaluation of forecasting models for time series with low predictability,” 2025, doi: 10.1007/s10489-025-06268-w.
J. Rieger, B. Liu, B. Saugel, and O. Grothe, “On the assessment of the ability of measurements, nowcasts, and forecasts to track changes,” BMC Med. Res. Methodol., vol. 24, no. 1, 2024, doi: 10.1186/s12874-024-02397-x.
Haeruddin, E. Noersasongko, Purwanto, and Muljono, “A Multi-Model Framework for Rainfall Forecasting: Evaluating Performance Model Statistical, Machine Learning, and Deep Learning Methods,” 2025 Int. Conf. Smart Comput. IoT Mach. Learn. SIML 2025, pp. 1–6, 2025, doi: 10.1109/SIML65326.2025.11080798.
H. Haeruddin, N. Ma’muriyah, and W. Wijaya, “Pengembangan System Berlangganan Aplikasi Di Usertip Menggunakan Stripe,” J. Inf. Syst. Manag., vol. 5, no. 2, pp. 127–133, 2024, doi: 10.24076/joism.2024v5i2.1288.
& Z. Pricillia, “Survey Paper : Perbandingan Metode Pengembangan Perangkat Lunak,” J. Bangkit Indones., vol. X, no. 01, pp. 6–12, 2021.
T. Tjahjanto, “Application of the Waterfall Method in Information System,” Sinkron, vol. 6, no. 4, pp. 2182–2192, 2022, [Online]. Available: https://jurnal.polgan.ac.id/index.php/sinkron/article/download/11678/1136/8044
H. J. Christanto and Y. A. Singgalen, “Analysis and Design of Student Guidance Information System through Software Development Life Cycle (SDLC) dan Waterfall Model,” J. Inf. Syst. Informatics, vol. 5, no. 1, pp. 259–270, 2023, doi: 10.51519/journalisi.v5i1.443.
R. A. Pratama and T. Desyani, “Implementasi Metode SDLC Waterfall Untuk Rancang Bangun Sistem Informasi Pengaduan Masyarakat Berbasis Web Dengan Notifikasi Telegram ( Studi Kasus : Kelurahan Bojongsari Baru , Kota Depok ),” OKTAL J. Ilmu Komput. dan Sci., vol. 3, no. 3, pp. 778–791, 2024.
F. K. Kartono et al., “Pengujian Black Box Testing Pada Sistem Website Osha Snack: Pendekatan Teknik Boundary Value Analysis,” J. Kridatama Sains Dan Teknol., vol. 6, no. 02, pp. 754–766, 2024, doi: 10.53863/kst.v6i02.1407.
J. Nadhifah et al., “Black Box Testing on the Wingpos Website Using the Equivalence Partitioning Technique,” Int. J. Inf. Eng. Sci., vol. 1, no. 4, pp. 81–88, 2024, doi: 10.62951/ijies.v1i4.128.
Suyudi et al., "Prediksi harga saham menggunakan metode Recurrent Neural Network. Prosiding Seminar Nasional Aplikasi Teknologi Informasi (SNATI)". Universitas Islam Indonesia. [Online]. Available: journal.uii.ac.id/Snati/article/view/13398
J. Cahyani, S. Mujahidin, & T.P. Fiqar, "Implementasi metode Long Short Term Memory (LSTM) untuk memprediksi harga bahan pokok nasional. JUSTIN (Jurnal Sistem dan Teknologi Informasi), vol. 11, no. 2, pp. 346-357, 2023.
A. Rosyd, A.I. Purnamasari, & I. Ali, "Penerapan metode long short term memory (LSTM) dalam memprediksi harga saham PT Bank Central Asia. JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 501-506, 2024.
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