• Burhanuddin Tryatmojo Universitas Paramadina
  • Raden Isum Suryani Maryati Universitas Paramadina


Face recognition systems is developing very quickly with various methods and algorithms. Face recognition system is an identification system that is developed based on differences in facial features biometrics on body parts or even human behavior. Face is used by systems to recognize someone because faces have different shapes and textures. Information about a person's face can be extracted from the image that has been detected. However, detection system still needs to be developed to obtain better results. Based on that background, this research formulated how much accuracy is generated in the face recognition detection system using the Haar Cascade algorithm, using a webcam camera and Python as its programming language. In this study, Raspberry Pi is used as a Single Board Computer to run a face recognition program and Open Computer Vision (OpenCV) as a face recognition library. This system called FaceTrix and from the results of testing the accuracy of the system, the best accuracy of the system is at a distance of 40 cm and 60 cm which was 100% and distance of 80 cm which was 91%. Based on overall system testing, the detection system's accuracy was 97% with an average time of 1.779 FPS.

Author Biographies

Burhanuddin Tryatmojo, Universitas Paramadina

Mahasiswa Program Studi Informatika Universitas Paramadina. Dapat dihubungi melalui alamat email burhanuddin.tryatmojo@gmail.com

Raden Isum Suryani Maryati, Universitas Paramadina

Mahasiswa Jurusan Informatika, Fakultas Ilmu Rekayasa, Universitas Paramadina.  Penulis dapat dihubungi pada alamat email radenisum@gmail.com


[1] S. Nugroho and A. Harjoko, “Sistem Pendeteksi Wajah Manusia Pada Citra Digital”, J. Sains dan Sibernatika, vol. 18, no. 1, 2005.
[2] Research Gate, "Detecting Faces in Images: A Survey," 2002. [Online]. Available: https://www.researchgate.net/publication/3193340_Detecting_Faces_in_Images_A_Survey.
[3] Jurnal EECCIS, "Sistem Pengenalan Wajah Secara Real-Time dengan Adaboost, Eigenface PCA & MySQL," 2013. [Online]. Available: https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/download/223/197
[4] Akmal, “Lebih Dekat Dengan Industri 4.0”, Sleman: Deepublish Publisher, 2019.
[5] Universitas Muhammadiyah Malang, Pengenalan Wajah dengan Cascade Classifier.
[6] W. S. Pambudi and B. M. N. Simorangkir, “Facetracker Menggunakan Menggunakan Metode Haar Like Feature dan PID Pada Model Simulasi”, J. Teknologi dan Informatika, vol. 2, no. 2, 2012.
[7] Eprints Polsri, “Rancang Bangun Sistem Audio Berbasis Raspberry Pi Via Bloethooth dengan Smartphone” 2016. [Online]. Available: http://eprints.polsri.ac.id
[8] Amazon, "Raspberry Pi," 2018. [Online]. Available: https://www.amazon.com/RSComponents-Raspberry-Pi-Motherboard/dp/B07BFH96M3.
[9] BINUS - Computer Science, "Penggunaan Python untuk Data Mining," 2018. [Online]. Available: http://socs.binus.ac.id/2018/11/16/penggunaan-python-untuk-data-mining/.
[10] Fire Flier, [Online]. Available: http://id.lamparaleds.com/news/how-much-lux-does-my-roomneed-18327451.html
[11] R. S. Pressman, in Software Engineering: A Practitioner Approach Seventh Edition, 2015.
[12] OpenCV, "OpenCV," 2018. [Online]. Available: https://opencv.org.
[13] Hackster.io, “Real-Time Face Recognition: An End to End Project”, [Online]. Available: https://www.hackster.io/mjrobot/real-time-face-recognitionan-end-to-end-project-a10826.
How to Cite
TRYATMOJO, Burhanuddin; MARYATI, Raden Isum Suryani. AKURASI SISTEM FACE RECOGNITION OPENCV MENGGUNAKAN RASPBERRY PI DENGAN METODE HAAR CASCADE. JURNAL ILMIAH INFORMATIKA, [S.l.], v. 7, n. 02, p. 92-98, oct. 2019. ISSN 2615-1049. Available at: <http://ejournal.upbatam.ac.id/index.php/jif/article/view/1354>. Date accessed: 01 oct. 2020. doi: https://doi.org/10.33884/jif.v7i02.1354.