HYBE Corporation Stock Price Prediction Using CNN-LSTM with CRISP-DM Framework
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Abstract
Digital transformation in financial analysis requires the application of computational models that can handle the complexity of the stock market efficiently and objectively. This study aims to predict the stock price of HYBE Corporation using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model within the CRISP-DM (Cross Industry Standard Process for Data Mining) framework. The data used consists of daily stock prices of HYBE Corporation obtained from Yahoo Finance. The research process includes the main stages of CRISP-DM, namely business understanding, data understanding, data preparation, modeling, evaluation, and presentation of results. The CNN-LSTM model is designed to combine the ability of CNN to extract local patterns from time series with the advantage of LSTM in capturing long-term dependencies. To maximize the parameters used, this study also performed hyperparameter tuning using GridSearchCV on several key parameters. This process aimed to obtain the best combination of parameters capable of improving prediction accuracy and reducing the error value in the CNN-LSTM model. The evaluation results show that the CNN-LSTM model is capable of providing predictions with a very high level of accuracy. The Mean Squared Error (MSE) value is 0.00029, the Root Mean Squared Error (RMSE) is 0.01704, the Mean Absolute Error (MAE) is 0.01346, and the Mean Absolute Percentage Error (MAPE) is 2.15%. These low evaluation values demonstrate the model's effectiveness in handling stock market volatility while maintaining stability in predicting both short-term and long-term patterns.
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