Vehicle image segmentation using mobile-based K-mean clustering to determine tax costs in supporting regional basic income

One component of local revenue is the motor vehicle parking levy tax in Bandar Lampung City which has not been optimal in its implementation. Currently, parking attendants experience several problems in collecting parking fees in the field, there is no automatic and real-time information on two-wheeled vehicles or motorcycles, and the processing of vehicle parking tax levies is not transparent. To overcome this problem, it is necessary to develop a system design that can assist parking officers in monitoring vehicles, this system will be facilitated by a vehicle parking monitoring tool in the form of a portable gadget. The method in this study starts from reading the vehicle number plate through a camera device then image data is processed using segmentation using the K-Mean clustering method, with preprocessing stages, image data normalization, calculating similarity, classification, segmentation training, testing, Result results. In building a system that is designed to be implemented in an environment with the relevant level of technological readiness, and this system will be facilitated by a vehicle parking monitoring device in the form of a portable gadget or commonly called “Parking Axis” which is easy to use by parking officers in collecting parking fees at field, as well as simplify and make the parking payment system more transparent and accountable.