Kipas Angin Pengikut Manusia Berdasarkan Wajah - Wahyudi Prasatia; Kamarudin Kamarudin (Politeknik Negeri Batam, 2018)
Technological advances make the need for an automated system increases, one of which makes the fan can track the human automatically. In this study to develop a fan system that can track human based on the face and can distinguish the face with other obstacles around it, so that humans do not need to manually set in directing the fan to get the wind from the fan. To detect human faces used cameras from android smartphones that have been integrated with the Haar Cascade detection method available in the OpenCV library. While to do tracking then used PID controller with Zigler Nichols tuning method which integrated with DC motor. Based on the results of experiments the system can track the human face with an average error of 4.14%, while testing on face detection has a 100% success rate in the face of the frontal. While the system can track human based on the face with 95% success. The error is caused by the mechanical system of dc motor gearbox and camera hardware position that is not straight to the face object.