RANCANG BANGUN ALAT PENGUNCI PINTU OTOMATIS BERBASIS PENGENALAN WAJAH MENGGUNAKAN RASPBERRY PI

siti Ayu Luffiyati Hamid; Ahwan Ahmadi; Hadian Mandala Putra; Hendra Setiawan; Ida Nurcahyani

doi:10.64626/jukomtek.v5i2.715

Penulis

siti Ayu Luffiyati Hamid Universitas Islam Indonesia
Ahwan Ahmadi Universitas Islam Indonesia
Hadian Mandala Putra Universitas Islam Indonesia
Hendra Setiawan Universitas Islam Indonesia
Ida Nurcahyani Universitas Islam Indonesia

DOI:

https://doi.org/10.64626/jukomtek.v5i2.715

Kata Kunci:

Raspberry Pi, Face Recognition, Local Binary Pattern Histogram (LBPH), OpenCV, Automatic Door Lock

Abstrak

Door security systems in government institutions generally still rely on conventional locks, which have several limitations, including the risk of being lost, easily duplicated, and the inability to automatically verify user identities. These limitations allow unauthorized visitors or individuals to enter restricted rooms without an authentication process, potentially disrupting office activities. This study aims to design and develop an automatic door locking system based on facial recognition using a Raspberry Pi as an alternative security solution. An experimental research method was employed by integrating a Raspberry Pi 3 Model B+, a USB camera, a 5V relay module, and a solenoid door lock as the hardware components, while the software was developed using the Python programming language, the OpenCV library, and the Local Binary Pattern Histogram (LBPH) algorithm for facial recognition. The proposed system detects a user's face and compares it with the registered facial database before automatically unlocking the door for authorized users. Experimental results indicate that the system achieved a facial recognition accuracy ranging from 40% to 90%, with performance influenced by lighting conditions, facial orientation, and the use of accessories such as eyeglasses. In addition, the system successfully controlled the automatic door locking mechanism according to the authentication results. Therefore, the developed system can serve as a practical and effective alternative to conventional door locking systems for improving access control in office environments

Referensi

Alifiansyah, I. et al. (2024) “Project Based Learning: Sistem Otentifikasi melalui Deteksi Wajah untuk Akses Pintu Otomatis Berbasis Raspberry Pi,” Jurnal Integrasi, 16(2), pp. 191–197. Available at: https://doi.org/10.30871/ji.v16i2.7646.

Andrean, M.N. et al. (2024) “Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection,” Jurnal Media Informatika Budidarma, 8(1), p. 272. Available at: https://doi.org/10.30865/mib.v8i1.7167. h

Arif, H.C. and Farell, G. (2024) “Perancangan Sistem Absensi Digital Berbasis RFID dan Face

Recognition,” 2(1), pp. 51–74. Available at: https://doi.org/10.24036/elektif.v2i1.44.

Elnozahy, S.S.F.A., Pari, S.C. and Liang, L.C. (2025) “Raspberry Pi-Based Face Recognition Door Lock System,” Internet of Things, 6(2). Available at:

https://doi.org/10.3390/iot6020031. https://www.mdpi.com/2624-831X/6/2/31

Irnayanti, R. and Elfizon, E. (2024) “Face Recognition Door Security System and Contactless Bell,” JTEIN: Jurnal Teknik Elektro Indonesia, 5(1), pp. 223–232. Available at: https://doi.org/10.24036/jtein.v5i1.650. http://jtein.ppj.unp.ac.id/index.php/JTEIN/article/view/650

Kosasih, R. and Daomara, C. (2021) “Pengenalan Wajah dengan Menggunakan Metode Local Binary Patterns Histograms (LBPH),” Jurnal Media Informatika Budidarma, 5(4), p. 1258. Available at: https://doi.org/10.30865/mib.v5i4.3171.

Nuraeni, N. et al. (2024) “Sistem Akses Pintu Berbasis Face Recognition Menggunakan ESP32 Module dan Aplikasi Telegram,” Jurnal MediaTIK, 4(3), pp. 115–119. Available at: https://doi.org/10.59562/mediatik.v4i3.3575. https://eprints.unm.ac.id/30027/

Saputra, R.R.S. et al. (2024) “Design and Build a Door Security System Using a Raspberry-Pi based Liveness Detection,” JURNAL JARTEL: Jurnal Jaringan Telekomunikasi, 14(4), pp. 400–407. Available at: https://doi.org/10.33795/jartel.v14i4.5949.

Septyanlie, V.V. et al. (2024) “Face Recognition-Based Door Lock Security System Using TensorFlow Lite,” Journal of Electrical Engineering and Computer (JEECOM), 6(2), pp. 402–409. Available at: https://doi.org/10.33650/jeecom.v6i2.9557.

Server, A.R. (2026) “JOURNAL CERITA :,” 8(225), pp. 14–23. https://elibrary.ru/item.asp?id=79002936

Subiyanto, S. et al. (2020) “Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi,” Jurnal Teknologi dan Sistem Komputer, 8(3), pp. 210–216. Available at: https://doi.org/10.14710/jtsiskom.2020.13590.

Zulfikar, R. et al. (2023) “Rancang Bangun Keamanan Pintu Otomatis Menggunakan Face Recognition Berbasis Internet Of Things (IoT),” JTEIN: Jurnal Teknik Elektro Indonesia, 4(2), pp. 445–453. Available at: https://doi.org/10.24036/jtein.v4i2.385. http://jtein.ppj.unp.ac.id/index.php/JTEIN/article/view/385