<![CDATA[Predicting consumer purchase intention in e-commerce is a crucial challenge due to the high rate of class imbalance, where the majority of visitors only browse without making a transaction. This study compares the performance of three Gradient Boosting family algorithms (XGBoost, LightGBM, and CatBoost) using the Online Shoppers Intention dataset, which has a class ratio of 84.5% to 15.5%. To overcome majority class bias, the Synthetic Minority Oversampling Technique (SMOTE) approach was implemented on the training data. This research focuses on hyperparameter optimization implementation using the Optuna framework based on the Tree-structured Parzen Estimator (TPE), which is statistically validated using the Friedman and Post-Hoc Nemenyi tests. Model evaluation using stratified 10-Fold Cross-Validation shows that all three models can handle class imbalance effectively. LightGBM achieved an accuracy of 88.36% with an ROC-AUC of 0.9138, XGBoost achieved an accuracy of 88.56% with an ROC-AUC of 0.9127, and CatBoost achieved an accuracy of 88.56% with an ROC-AUC of 0.9121. Feature importance analysis identifies ProductRelated_Duration and ExitRates as the main predictors of purchase intention. The Friedman statistical test detected global performance differences (p=0.0450), but the Nemenyi post-hoc test found insufficient empirical evidence to claim significant pairwise performance differences. This research provides a practical contribution to the e-commerce industry by demonstrating that the selection of ensemble algorithms no longer needs to rely absolutely on pseudo-accuracy margins, but can be objectively recommended based on computational latency efficiency, where the LightGBM architecture proves to be efficient.]]>
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