Banking Stock Price Prediction Dashboard Using Long Short-Term Memory

Ilma Navila; Noora Qotrun Nada; Ramadhan Renaldy

doi:10.52436/1.jutif.2026.7.2.5802

Authors

Ilma Navila Informatics, Universitas PGRI Semarang, Semarang, Indonesia
Noora Qotrun Nada Informatics, Universitas PGRI Semarang, Semarang, Indonesia
Ramadhan Renaldy Informatics, Universitas PGRI Semarang, Semarang, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5802

Keywords:

CRISP-DM, Deep Learning, Long Short-Term Memory, Stock Prediction

Abstract

The high volatility of banking sector stocks (BBCA, BBRI, BMRI) and the limitations of conventional forecasting methods in handling non-linear data necessitate robust and adaptive predictive models. This study aims to develop an integrated stock price prediction system utilizing a Stacked Long Short-Term Memory (LSTM) architecture embedded within a Flask-based interactive web dashboard. Adopting the CRISP-DM framework, the model was trained using daily and hourly historical data from Yahoo Finance to accommodate both short-term and medium-term forecasting. Backtesting evaluation demonstrated that the LSTM model achieved Mean Absolute Percentage Error (MAPE) values below 2% for daily single-step predictions and below 0.5% for hourly intraday predictions. Furthermore, in a 7-period recursive projection, the proposed LSTM proved highly robust in mitigating error accumulation compared to Linear Regression and Support Vector Regression (SVR), successfully maintaining MAPE values below 5% for all issuers. The implementation of this dashboard system provides a significant impact on financial informatics by bridging advanced deep learning predictive algorithms into a practical, real-time decision support system for investment analysis.

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