Prototype Program Hand Gesture Recognize Using the Convex Hull Method and Convexity Defect on Android - Anshary, Muhammad Adi Khairul; Hidayat, Eka Wahyu; Amalia, Tiara (Department of Informatics, UIN Sunan Gunung Djati Bandung, 2020)
One of the research topics of Human-Computer Interaction is the development of input devices and how users interact with computers. So far, the application of hand gestures is more often applied to desktop computers. Meanwhile, current technological developments have given rise to various forms of computers, one of which is a computer in the form of a smartphone whose users are increasing every year. Therefore, hand gestures need to be applied to smartphones to facilitate interaction between the user and the device. This study implements hand gestures on smartphones using the Android operating system. The algorithm used is convex hull and convexity defect for recognition of the network on the hand which is used as system input. Meanwhile, to ensure this technology runs well, testing was carried out with 3 scenarios involving variable lighting, background color, and indoor or outdoor conditions. The results of this study indicate that Hand gesture recognition using convex hull and convexity defect algorithms has been successfully implemented on smartphones with the Android operating system. Indoor or outdoor testing environment greatly affects the accuracy of hand gesture recognition. For outdoor use, a green background color with a light intensity of 1725 lux produces 76.7% accuracy, while for indoors, a red background color with a light intensity of 300 lux provides the greatest accuracy of 83.3%.