<![CDATA[Motor vehicle testing is a series of activities to check the components in the vehicle. Motor vehicle testing is very important, because vehicles operated on the road have the potential to cause accidents. So if periodic inspections are not carried out, it cannot provide technical safety to vehicle users, because it is not known what components are lacking and what components must be repaired. In this study, motor vehicle test data will be calculated using the K-Means. The K-means algorithm is aclusteralgorithm non-hierarchical. Cluster analysis is a tool for grouping data based on variables or features. The purpose of k-means clustering, like other clustering methods, is to obtain clusters of data by maximizing the similarity of characteristics within the cluster and maximizing the differences between clusters.groups K-means clustering algorithm data based on the distance between the data and the centroid cluster Cluster with the number of motorized vehicle data based on the type of vehicle as many as 4 vehicles, namely, freight cars, MPU, buses, and betor. Cluster 1 there are 7 groups with 7 types of vehicles: 2 BUS and 5 betor where there is one type of vehicle (betor) that does not pass the test due to the type of damage at the time of testing motor vehicle. Cluster 1 is the type of vehicle that passes the motor vehicle test the most with the lowest level of damage; Cluster 2 there are 4 groups by type of vehicle: 4 Cars of Freight where 2 of them did not pass the test because of the type of damage during the motor vehicle test; Cluster 3 has 9 groups with the types of vehicles: 2 freight cars, 4 MPUs, and 3 BUS. 3 BUS and 1 MPU did not pass the test due to damage during the motor vehicle test. cluster is the cluster of vehicle types that do not pass the test with the most types of damage.]]>
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