Comparative Analysis of Linear Discriminant Analysis and Support Vector Machine Methods in an Automated Cash Parking Payment System
DOI:
https://doi.org/10.33795/jartel.v16i3.11148Keywords:
Cash Payment, Support Vector Machine (SVM), Linear Discriminant Analisys (LDA), Raspberry Pi, Automated Parking SystemsAbstract
One innovation that can be implemented is an automated parking system with an automatic cash payment method. This system allows vehicle users to conduct payment transactions without direct interaction with parking attendants, through a payment machine integrated with the parking entry and exit control system. This mechanism not only speeds up the parking process but also minimizes human error and enhances transparency in transaction recording. In this research, a cash payment system for an automated parking system will be developed that is capable of accepting Indonesian rupiah banknotes, recognizing banknote denominations and verifying their authenticity, calculating the total transaction amount, and providing change in the form of banknotes. The automated cash payment system in the parking system consists of a banknote acceptance mechanism, a banknote dispensing mechanism, and a banknote denomination recognition system. Banknote denomination recognition is performed based on banknote image analysis. The research objects used in banknote recognition are Indonesian rupiah banknotes issued in 2022 with denominations of IDR 20,000, IDR 10,000, and IDR 5,000. The banknotes provided as change in this system are IDR 10,000 and IDR 5,000 denominations. The system's performance test results indicate that the image processing unit, utilizing the Support Vector Machine (SVM) algorithm, achieves high precision in banknote identification. In testing the 2022 emission banknotes for the Rp5,000, Rp10,000, Rp20,000, and Rp50,000 denominations, the device reached an accuracy rate of 98%. Furthermore, when tested against counterfeit money samples obtained from Bank Indonesia, the system demonstrated a 100% accuracy rate in detection. Additionally, the system showed perfect reliability in transaction logic, where the calculation and dispensing of the correct change achieved a 100% accuracy rate
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