<![CDATA[Traffic congestion remains one of the problems that continue to arise, especially in urban areas, oneof which is Bandung City, when the causes of the problem are not managed properly. Continuousmanagement of the causes of congestion problems will result in a controlled traffic system for theforeseeable future. This condition can be achieved if there is a congestion classification predictionsystem available. A reliable prediction and classification system can support the government informulating data-based traffic management strategies. The Random Forest and K-NearestNeighbour machine learning classification methods are strengthened with time-based featureexpansion to capture traffic behavior in various time frames, so that the objectives can be achieved.The dataset obtained from Area Traffic Control System Bandung includes traffic flow recorded at15-minute intervals at several intersections. Additional features such as red light duration, roadwidth, and spatial proximity to residential and commercial areas are included to improve modelperformance. The results show that the Random Forest classifier with time-based feature expansionoutperforms K-Nearest Neighbors, achieving the highest performance of 96%. These results showthe potential contribution in short-term traffic prediction and its effectiveness in supporting urbantraffic planning and congestion mitigation efforts in Bandung.]]>
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