Open AccessOpenCV and Machine Learning Implementation for the Vehicles Classification and Calculation in the Parking Tax Monitoring System at the Bantul Regency Regional Financial and Asset Agency (BKAD)
Author(s) -
D Agustiani,
Susilo Wardani,
Adhi Slamet Riyadi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1823/1/012062
Subject(s) - taxpayer , officer , business , revenue , government (linguistics) , finance , agency (philosophy) , asset (computer security) , local government , computer security , computer science , economics , law , linguistics , philosophy , epistemology , political science , macroeconomics
After regional autonomy has been implemented in Indonesia, local governments must maximize its revenue through various sectors, including the Bantul Regency government. One source of regional income is a local tax, based on law no 28 of 2009, one type of tax-managed by local government is the parking tax. Either an off-street parking lots, those provided in connection with the business principal, those provided as a business, or motor vehicle daycare (Indonesian Law No.28 of 2009). The parking tax is a self-assessment tax. Taxpayers will calculate their amount of tax that must be paid to the government. Therefore, the local government should conduct oversight of the reports of taxpayers. One form of supervision carried out is to monitor the number of vehicles at the taxpayer’s location. Officers then record the number of vehicles based on vehicle classification, whether they were two-wheeled or four-wheeled vehicles or other vehicle types. Currently, monitoring is done manually using a mechanical counter. This monitoring may have the risk of being miscalculated or wrongly recorded due to the monitoring officer’s oversight. Computer vision (OpenCV library) and machine learning (Mask R-CNN), is expected to minimize these errors and optimize the officers’ performance on duty.