Implementasi Sistem Keamanan Brankas Berbasis Face Recognition Menggunakan Algoritma YOLO dengan Verifikasi Fingerprint

Authors

  • Amanda Tsabita Putri Tata Politeknik Negeri Sriwijaya
  • Irma Salamah Politeknik Negeri Sriwijaya
  • Martinus Mujur Rose Politeknik Negeri Sriwijaya

DOI:

https://doi.org/10.37802/joti.v7i2.1072

Keywords:

Face Recognition, Fingerprint, Security, YOLO

Abstract

Current technological developments make security crucial in protecting important documents. Safes are commonly used for storage but remain vulnerable to burglary despite conventional locks. Based on data from the Central Bureau of Statistics (BPS) in 2023, aggravated theft was the most frequent crime with 62,872 reported cases. This highlights the need to improve safe security by adding biometric techniques such as face recognition and fingerprint verification. This study proposes a layered security system combining face recognition using the YOLO algorithm and fingerprint sensors. The system uses an ESP32-CAM to capture facial images and an ESP32 microcontroller to control a solenoid lock, fingerprint sensor and buzzer alarm. Face recognition testing on two users showed, the trained YOLO model achieved an accuracy of 83.33%, precision of 83,33% and recall of 100%.  from 12 trials, with two failures due to poor lighting conditions. Fingerprint testing on 10 samples, five fingers from each of two users, showed successful recognition of all fingerprints with an average response time of 1.41 seconds. The integration of face and fingerprint biometrics significantly enhances safe security and minimizes unauthorized access risks.

Downloads

Download data is not yet available.

Author Biographies

Irma Salamah, Politeknik Negeri Sriwijaya

Dosen Politeknik Negeri Sriwijaya, Jurusan Teknik Elektro Program Studi D4 Teknik Telekomunikasi

Martinus Mujur Rose, Politeknik Negeri Sriwijaya

Dosen Politeknik Negeri Sriwijaya, Jurusan Teknik Elektro Program Studi Teknik Telekomunikasi

References

M. Ilham Ali, S. Adi Wibowo, and A. Panji Sasmito, “Keamanan Brankas Menggunakan E-Ktp Dan Notifikasi Via Telegram Berbasis Iot (Internet of Things),” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 5, no. 2, pp. 589–596, 2021, doi: 10.36040/jati.v5i2.3793.

O. R. Arsyad and K. P. Kartika, “Rancang Bangun Alat Pengaman Brankas Menggunakan Sensor Sidik Jari Berbasis Arduino,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 5, no. 1, pp. 1–6, 2021, doi: 10.36040/jati.v5i1.3285.

BPS (Badan Pusat Statistik), “Statistik Kriminal 2024,” vol. Volume 15, 2024.

P. Syahbana, “Terlilit Pinjol, Karyawati Alfamart di Sumsel Bobol Brankas-Gasak Rp 60 Juta”, [Online]. Available: https://www.detik.com/sumut/hukum-dan-kriminal/d-6559008/terlilit-pinjol-karyawati-alfamart-di-sumsel-bobol-brankas-gasak-rp-60-juta

J. M. Immanuel, I. Ibrahim, R. Rahmadewi, and Y. Saragih, “IoT-based Facelook and Fingerprint Safe Security System,” Jurnal Penelitian Pendidikan IPA, vol. 10, no. 2, pp. 500–505, 2024, doi: 10.29303/jppipa.v10i2.6832.

D. Ayu, D. Syaiful, and A. Ramelan, “Rancang Bangun Alat Sistem Absensi Mahasiswa menggunakan Face Recognition dengan Metode YOLO berbasis Raspberry Pi,” vol. 6, no. 2, 2024. doi: 10.26905/jasiek.v7i2.14144

H. Putri et al., “Security System for Door Locks Using YOLO-Based Face Recognition,” International Journal on Informatics Visualization, vol. 9, no. 1, pp. 224–230, 2025, doi: 10.62527/joiv.9.1.2410.

I. Salamah, M. R. A. Said, and S. Soim, “Perancangan Alat Identifikasi Wajah Dengan Algoritma You Only Look Once (YOLO) Untuk Presensi Mahasiswa,” Jurnal Media Informatika Budidarma, vol. 6, no. 3, p. 1492, 2022, doi: 10.30865/mib.v6i3.4399.

M. Fauzan Alfiandi, F. Utaminingrum, and E. Rosana Widasari, “Perancangan Sistem Pengamanan Ganda pada Brankas menggunakan Convolutional Neural Network berbasis Raspberry Pi,” … Teknologi Informasi dan …, vol. 6, no. 9, pp. 2548–964, 2022, [Online]. Available: http://j-ptiik.ub.ac.id

N. Chalista et al., “Identifikasi Pengenalan Wajah Berdasarkan Jenis Kelamin Menggunakan Metode Convolutional Neural Network ( CNN ) Journal of Technology and Informatics (JoTI),” vol. 6, no. 1, 2024, doi: 10.37802/joti.v6i1.694.

M. R. Daffa Ulhaq, M. A. Zaidan, and D. Firdaus, “Pengenalan Ekspresi Wajah Secara Real-Time Menggunakan Metode SSD Mobilenet Berbasis Android,” Journal of Technology and Informatics (JoTI), vol. 5, no. 1, pp. 48–52, 2023, doi: 10.37802/joti.v5i1.387.

M. Adrezo, M. E. Ardiansyah, and P. S. Informatika, “Deteksi Jenis Kelamin Berdasarkan Wajah Menggunakan Metode YOLOv8 Gender Detection Based on Face Using The YOLOv8 Method 1,2,” vol. 7, no. 2019, pp. 1757–1762, 2024. doi: 10.31539/intecoms.v7i5.12482

N. Prasetyo, F. Gozali, E. Djuana, and R. Rambung, “Sistem Brankas Menggunakan Pengenalan Wajah Berbasis Raspberry PiI,” Jetri : Jurnal Ilmiah Teknik Elektro, vol. 19, no. 1, pp. 60–76, 2021, doi: 10.25105/jetri.v19i1.10005.

Downloads