Increasing Passive RFID-Based Smart Shopping Cart Performance using Decision Tree

摘要

This paper proposes the smart shopping cart based on passive RFID. While on peak season market, queues are often found at clothing store. This happens because the service time between consumers takes a long time; the cashier must scan all items one by one, and make the transaction process at the cashier slow. In proposed system, each item be equipped with an RFID tag, and the shopping cart be equipped with an RFID reader, so the items that are inserted into the shopping cart will be scanned and calculated in the system. This system makes the service time shorter and minimize the number of queues at the cashier. To support the performance of smart shopping cart, a decision tree algorithm is implemented for classifying consumer shopping lists and determine discounts. The author tests and analyzes the performance of the decision tree ID3 algorithm in the smart shopping cart. The test results show that decision tree algorithms can determine discounts with a 90% accuracy rate and 100% precision rate