<![CDATA[Certain strains of Escherichia coli (E. coli) can cause serious illness, so identifying dangerous strains with high accuracy is a priority in supporting public health and food safety. However, traditional machine learning methods, such as Decision Trees, are often not robust enough to handle the complexity of biological data. This research presents a solution by systematically evaluating seven ensemble methods, namely Adaboost, Gradient Boosting, XGBoost, LightGBM, Random Forest, Bagging, and Stacking, using a dataset that includes 336 E. coli samples with eight biological features. These models are evaluated based on accuracy, precision, recall, and F1 score, with parameter optimization to obtain the best results. The results show that XGBoost is superior with accuracy, recall, and F1 score of 88% and precision of 87%, outperforming other methods. This research has the advantage of a comprehensive approach in comparing various ensemble methods simultaneously, accompanied by the application of confusion matrix-based evaluation to ensure the accuracy of the results. Additionally, the ensemble approach proved to be more effective in handling complex data patterns and reducing bias in bacterial strain classification. These findings provide a significant contribution, namely a practical framework for improving laboratory diagnostics and public health surveillance, with machine learning-based solutions that are faster, more reliable, and applicable for both industrial and clinical environments. This research expands understanding of the potential of ensemble methods in microbiological data classification and provides new directions for modern diagnostic technology.]]>
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