<![CDATA[Background: Traffic congestion in Badung Regency, Bali, presents complex challenges for transportation management in a strategic tourism area, necessitating intelligent monitoring methods that surpass conventional surveys.Aim: This study develops a Hybrid Machine Learning framework that integrates unsupervised and supervised learning techniques to map spatial patterns and precisely predict congestion anomalies.Methods: Initially, the K-Means Clustering algorithm validated the segmentation of traffic data into five optimal clusters (k=5) based on Elbow and Silhouette Score tests, which subsequently served as the ground truth for data labeling. Furthermore, a comparative evaluation of Random Forest, XGBoost, and Gradient Boosting models was conducted to determine the best predictive performance.Result: Experimental results identified Random Forest with strict pruning parameters (max_depth=3) as the optimal model, balancing accuracy and generalization stability while effectively avoiding overfitting through learning curve analysis. This model achieved a testing accuracy of 92.00% and a cross-validation score of 89.69%, with the Travel Time Index (TTI) and average speed identified as the primary determinants.Conclusion: Spatial visualization demonstrated high consistency between the model's prediction maps and actual field conditions, confirming that this approach is effective as a foundation for an Early Warning System to support data-driven traffic management policies in Bali.]]>
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