Kristiana, Lisa and Aditya, Hafidz Dayu The Viability of Leap Motion Implementation in Controlling Drone using K-Nearest Neighbor Algorithm. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8 (3). pp. 683-692. ISSN 2338-8323 (cetak) dan 2459-9638 (elektronik)

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Abstract

Controlling a drone can be more entertaining and flexible by using a hand gesture compare to the conventional mode by using a joystick. However, a drone controlling using the hand gestures produce a large number of data sets that drive the drone’s movements in particular. For this reason, a Leap Motion Controller is required to record and recognize the hand pose samples and extract the data sets. Our approach is to use the K-Nearest Neighbor (KNN) algorithm as our method in order to classify the x, y, z, Pitch, Roll and Yaw values which are based on the conventional aircraft motions. This research focuses on the accuracy value of implementing the Leap Motion device to control a drone with the KNN algorithm. The result shows that the k-values from 3 obtain 72.8% of accuracy.

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