Quality of Service Analysis in Hand Gesture Identification Application Based on Convex Hull Algorithm to Control a Learning Media

Abstract

The technique of human interaction with computers has developed very rapidly. Body movement is the easiest and most expressive way and hand movements are flexible. We can use gestures as a simple identification command. This research discusses the application of hand gesture reading as a remote control for learning media and analyzes its quality of service using Wireshark software. This system uses a Raspberry Pi as a computing center. Raspberry Pi reads hand gestures using the Convex Hull algorithm, which can read the number of fingers raised on the hand by taking the outermost point in the contour scan of the hand. Each gesture in the form of the number of fingers was allocated to the keyboard commands used to select answers on the learning media. On the learning media side, a quiz system was created that uses keyboard commands to select the answers. Then the Raspberry Pi is connected to the laptop using a third-party application called VNC. Based on QoS measurement results, the throughput result is 62 kbps, which is included in the very good category. Packet Loss of 0%, which is also included in the very good category. The delay of 52.2 ms, which is included in the very good category. Thus, the overall quality of the network can be categorized as very good.