Implementasi K-Nearest Neighbor untuk Klasifikasi Ekspresi Wajah Berdasarkan Data Muscle Sensor dan Berbasis Arduino - Aprilo Paskalis Polii; Hurriyatul Fitriyah; Issa Arwani (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Human facial expressions are formed by face muscles. Therefore, as an interest to develop Human-computer interaction, the system of human facial expression classification based on face muscles' movement is made for those reasons. The output from facial muscles is obtained by the muscle sensor. The classification in this research has been done by using K-Nearest Neighbor Algorithm system. The Muscle sensor is connected to the face by using electrodes. Then, the sensor's output is processed in Arduino and shows the result on LCD Monitor as an output. By the testing of sensor's functionality, it is found that the sensor responds according to the muscle performance. The sensor's value is increased along with the number of gained loads. Besides that, by the testing of LCD monitor's functionality, the result is obtained that LCD Monitor works well by displaying the output in accordance with the command. Then by the accuracy testing, the best the result is from K equals to 3 with 81% of accuracy level. By the computation time testing, the result of taking the output from sensor, processing, and display the classification takes 1.68 seconds as the average time.