IMPLEMENTASI SISTEM KEAMANAN RUMAH BERBASIS FACE RECOGNITION DENGAN PERINGATAN ALARM OTOMATIS MENGGUNAKAN METODE LOCAL BINARY PATTERNS HISTOGRAM
DOI:
https://doi.org/10.59134/jsk.v11i1.765Keywords:
Home Security, Face Recognition, LBPH, ESP32-CAM, Telegram, IoTAbstract
This research aims to design and implement a home security system based on facial recognition that is capable of working in real-time and integrated with the Internet of Things (IoT), in order to overcome the limitations of conventional security systems that are not yet able to detect and respond to potential threats automatically and still rely on manual supervision. The system was developed using an ESP32-CAM module with a Local Binary Pattern Histogram (LBPH) algorithm for the facial identification process, and is integrated with a buzzer and the Telegram application as a two-layer warning system. The method used is prototyping with an iterative approach for two months through direct testing in a residential environment. The test results show that the system is able to recognize faces with 95.42% accuracy, provides a fast response, and works stably in various lighting conditions. The conclusion of this research shows that the system is effective, economical, and can be implemented without major changes to the building structure. This system also shows potential for further development on a more complex smart home scale.
References
[1] Waroh, R., Makarawung, H., & Tiwow, M. (2024). Penerapan Teknologi Pengenalan Wajah untuk Sistem Keamanan Rumah Cerdas. Jurnal Teknologi Informasi dan Keamanan, 15(1), 45–52.
[2] Siregar, A. A., Periyadi, & Hapsari, G. I. (2023). Evaluasi Sistem Keamanan Rumah Konvensional di Era Digitalisasi. Jurnal Sistem dan Teknologi Informasi, 12(2), 101–108.
[3] Muhana, F., & Fuad, M. (2024). Smart Home Security System berbasis Otomasi dan Deteksi Wajah. Prosiding Seminar Nasional Teknologi dan Inovasi, 2024, 88–95.
[4] Aji Pangestu, Galuh, & Moch Yusuf Asyhari. (2024). Sistem keamanan rumah berbasis Internet of Things (IoT) menggunakan notifikasi bot Telegram untuk pendeteksian gerak. Jurnal X, 4(1).
[5] Andi Abdullah Siregar, Periyadi, & Gita Indah Hapsari. (2023). Sistem keamanan ruangan pribadi menggunakan QR code dan Telegram monitoring berbasis Internet of Things.
[6] Fadlurahman, M. (2024). Implementasi face recognition berbasis Local Binary Pattern Histogram dalam sistem keamanan rumah. Jurnal Teknologi Keamanan, 10(2), 123–134.
[7] Fahd Al-Mutawa, Rihab, & Fathy Albouraey Eassa. (2020). A smart home system based on Internet of Things. International Journal of Advanced Computer Science and Applications (IJACSA), 11. Retrieved from www.ijacsa.thesai.org
[8] Fatih Muhana, Muhammad, & Evans Fuad. (2024). Keamanan. Jurnal Mahasiswa Teknik Informatika, 8.
[9] Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20. https://doi.org/10.1109/TCSVT.2003.818349
[10] Liu, L., & Li, K. (2021). Optimization of image buffering using PSRAM on ESP32-CAM platform. Journal of Embedded Systems.
[11] Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164–173. https://doi.org/10.4236/jcc.2015.35021
[12] Nugroho, D. R. (2018). Computer vision dalam sistem keamanan rumah. Jurnal Pengolahan Citra, 14(3), 67–78.
[13] Nugroho, M. A., Kuswara, R. P., & Kusumoputro, B. (2018). Convolution neural network for pose estimation of noisy three-dimensional face images. In Proceedings of the 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. xx–xx). 22–23 November 2018.
[14] Ojala, T., Pietikäinen, M., & Harwood, D. (2002). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1), 51–59.
[15] Purno, Ari, & Wahyu Wibowo. (2016). Implementasi teknik computer vision dengan metode colored markers trajectory secara real time. Jurnal Teknik Informatika, 8.
[16] Purno, B. H., & Wibowo, A. (2016). Deep learning dalam pengenalan pola gambar. Jurnal Teknologi Visual, 8(2), 45–58.
[17] Santoso, J. T., Agus Wibowo, & M. Si. (n.d.). Keamanan sistem tertanam (Embedded system security).
[18] Santoso, P. W., Piarsa, I. N., & Marini Mandenni, N. M. I. (2021). Sistem keamanan helm berbasis Internet of Things dengan fitur pelacakan menggunakan Android. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(5), 967–976. https://doi.org/10.29207/resti.v5i5.3507
[19] Stepanus, R. S., Beni Satria, & Ahmad Dani. (2025). Instal: Jurnal Komputer Implementation of LBPH algorithm in automatic door opening system with facial recognition. https://doi.org/10.54209/jurnalinstall.v16i06.349
[20] Sundararajan, S. (2021). Biometric authentication: Konsep dan aplikasi. International Journal of Biometrics and Security, 8(1), 15–28.
[21] Suwardono, A., & Puspitasari, D. (2023). Implementasi mikrokontroler ESP32 dalam sistem IoT keamanan rumah.
[22] Suwardono, A., & Puspitasari, M. D. M. (2023). Menguasai ESP32: Dasar, jaringan, sensor, dan proyek IoT. Lima Aksara.
[23] Waroh, A. P. Y., Sajangbati, N., Sawidin, S., Kondoj, M. A. S., & Wungkana, T. J. (n.d.). Sistem keamanan rumah melalui pengenalan wajah dengan webcam berbasis Raspberry Pi4. Jambura Journal of Electrical and Electronics Engineering, 7.
[24] Zhao, W., Chellappa, R., & Phillips, P. J. (2021). Face recognition: Advances in biometric recognition systems (2nd ed.). Springer.
[25] Szeliski, R. (2020). Computer vision: Algorithms and applications (2nd ed.). Springer.
[26] Ahonen, T., Hadid, A., & Pietikäinen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041. https://doi.org/10.1109/TPAMI.2006.244
[27] Putra, R. D. (2020). Internet of Things (IoT): Konsep dan Implementasinya dalam Kehidupan Sehari-hari. Yogyakarta: Deepublish.
[28] Lutz, M. (2013). Learning Python (5th ed.). O’Reilly Media. Müller, A., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media.
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