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May 26, 2025
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September 30, 2025
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Abstract

Predicting stock prices is an important financial topic, especially for investors who want to maximize profit and minimize risk. This research compares two machine-learning capabilities, a Recurrent Neural Network (RNN) and an Extreme Learning Machine (ELM), in predicting Bank Cental Asia (BBCA) stock prices. These two are chosen for their capabilities in handling time-series data. This research uses the data of BBCA’s daily prices over a certain period and involves several steps such as data collecting, data pre-processing, model training, and calculation of accuracy value. This accuracy calculation will be evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). This research shows ELM has better accuracy than RNN in predicting BBCA’s stock prices. ELM shows lower MSE and MAPE values than RNN, indicating the capability of ELM to predict with smaller errors. This research also concludes ELM is better in accuracy than RNN in predicting BBCA’s stock prices. Thus, ELM is the recommended method to predict stock prices.

Keywords

Artificial Neural Network Bank Central Asia Extreme Learning Machine Recurrent Neural Network Stock Prediction

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