Classification of Stock Listing Boards for Warrants using Machine Learning and Bayesian Optimization

Automatic classification of warrant stock listing boards is an important challenge in managing capital market information, especially on the Electronic Indonesia Public Offering (E-IPO) platform. This research implements various machine learning algorithms optimized using Bayesian Optimization to improve the classification accuracy of six listing board categories. Ensemble models such as Random Forest, CatBoost, and XGBoost showed superior performance with the highest accuracy reaching 74.68%. The use of Bayesian Optimization effectively finds the optimal hyperparameters, strengthening the overall performance of the model. Evaluation was conducted through stratified cross-validation and confusion matrix analysis, providing in-depth insight into prediction accuracy. The results of this research contribute to the automation of listing board clustering that supports the strategic decisions of investors, issuers, and regulators in the Indonesian capital market.