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May 28, 2026
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Abstract

Stock price prediction remains one of the most demanding challenges in financial analytics due to the inherently non-linear and temporally dependent nature of market data. This study proposes a deep learning framework based on Simple Recurrent Neural Network (SimpleRNN) for next-day closing price prediction of PT Telkom Indonesia (Persero) Tbk (TLKM.JK), one of Indonesia's most actively traded blue-chip stocks on the Indonesia Stock Exchange (IDX). The model was trained on 4,434 daily OHLCV observations from 2008 to 2026 sourced via Yahoo Finance, augmented with six engineered features encompassing momentum, trend, and volatility indicators. A sliding window of ten timesteps was employed to encode sequential dependencies. To ensure result reliability, the model was evaluated across five independent runs under identical hyperparameter configurations. The mean performance metrics were: MAE = Rp 67.50, RMSE = Rp 89.13, MAPE = 2.00%, and R² = 0.9775, indicating high predictive accuracy and cross-run stability. Error analysis revealed a near-symmetric distribution (skewness = 0.029) with a slight overestimation tendency (56.4%). The trained model was subsequently integrated into a FastAPI-based REST endpoint with SQLite persistence and an interactive multi-range candlestick dashboard, forming a deployable investment decision-support prototype. These findings demonstrate that SimpleRNN, when combined with systematic feature engineering and robust deployment architecture, can serve as a practical and computationally efficient tool for short-term equity price forecasting in emerging digital business contexts.

Keywords

deep learning investment decision support recurrent neural network stock price prediction time series forecasting

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How to Cite
Syamsu Alam, Muh. Nur Syamsi Hidayah, Muh. Ashif, Rizal Bakri, & Muh. Qardawi Hamzah. (2026). Deep Learning for Investment Decision Support: A SimpleRNN Approach to Stock Price Prediction with Real-Time API Deployment. Online Journal of Management, Innovation, Economics, and Digital Studies, 1(1), 16–26. Retrieved from https://onmind.bisdig.feb.unm.ac.id/article/view/637
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