Machine Learning uses ensemble methods, where the smaller models that are combined to form the ensemble model are referred to as basic models. Ensemble methods often result in considerably higher performance than any of the individual base models could achieve. The problem with the study is that the results of previous studies of car quality prediction using the K-means algorithm was by classifying the six characteristics of car specifications (buying, maint, doors, persons, lug_boot, safety), so that its quality is evaluated and the result using the K-means algorithm is Only 56%. so, this study presents a description of the quality assessment of cars based on the random forest algorithm by classifying the six characteristics of the car specifications, so that their quality is evaluated by the Python program. In this research, the ensemble method is called bagging, which is short for bootstrap aggregating. Bagging builds multiple base models with resampled training data with replacement. During the analysis and comparison of the results of the random forest algorithm with the results of the k-means algorithm, the results of the random forest algorithm were better than the k-means algorithm with the same data set, the accuracy of the random forest algorithm was 98.07%, while the accuracy of the k-means algorithm was 56%. ................. Keywords:.............. random forest, automobile quality, K-means algorithm.