<![CDATA[Conveyors are used by many factories in the industrial sector as tools to move some materials through various processes. Currently, it is necessary to have a device which is connected to a conveyor using a digital system. In this study, a conveyor is designed to use a webcam with a deep learning image classification system, Firebase real-time database, and a web-based dashboard. The webcam is used to capture and classify objects based on shape, color, and status, as well as counting objects that run on the conveyor. Firebase real-time database will receive and store data from the webcam system in real-time so that the data can be displayed on the dashboard. The dashboard used is a website-based design using two web development systems: front-end and back-end. Data displayed on the dashboard uses a real-time data table which is capable of displaying real-time data. Testing is conducted to analyze the performance of the full prototype. Testing methods used are One-by-one Object Test and Sequential Object Test, with total of 20 tests. One-by-one Object test is conducted five times, with a total of 168 data and a total time of 12 minutes and 15 seconds. Meanwhile, Sequential Object test is conducted 15 times, with a total of 546 data and a total time of 7 minutes and 19 seconds. Based on the observations of functional dashboard test, in fact all features and buttons on the dashboard are functioned well.]]>
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