RANCANG BANGUN SISTEM KEHADIRAN SECARA REAL TIME MENGGUNAKAN FACE RECOGNITION DENGAN METODE SSD DI SMK NEGERI 53 JAKARTA

Azizah Azzahra, Fitri Elvira Ananda

Abstract


SMK Negeri 53 Jakarta melakukan pengembangan terhadap pengelolaan data kehadiran di lingkungan sekolah. Pengembangan yang dilakukan adalah membuat sebuah sistem kehadiran menggunakan teknologi pengenalan wajah secara realtime dengan menggunakan metode Single Shot MultiBox Detector (SSD). Metode SSD merupakan salah satu metode pendeteksian dan pengenalan objek dengan deep learning, yang dapat digunakan untuk mendeteksi wajah secara cepat dan akurat.  Penelitian dilakukan dengan menggunakan kamera yang terintegrasi dengan website sistem kehadiran yang sudah ditanam algoritma SSD. Hasil pengujian menunjukkan bahwa sistem kehadiran yang sudah dirancang mampu melakukan pendeteksian dan pengenalan wajah dengan nilai tertinggi untuk tingkat akurasi 100%, nilai recall 100%,  nilai pendeteksian wajah 80%, nilai pengenalan wajah 90%, nilai efisiensi dalam segi waktu 95.8%, dan nilai efisiensi dengan sistem kehadiran lainnya sebesar 86%. Dengan mengimplementasikan teknologi pengenalan wajah menggunakan metode SSD pada sistem absensi akan memberikan efisiensi dalam segi waktu, dan proses pendataan serta pengolahan data kehadiran.  

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References


Liu, Y., Liu, R., Wang, S., Yan, D., Peng, B., & Zhang, T. (2022). Video face detection based on improved SSD model and target tracking algorithm. Journal of Web Engineering.

https://doi.org/10.13052/jwe1540-9589.21218.

Hu, X., & Huang, B. (2020). Face detection based on SSD and CamShift. 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).

https://doi.org/10.1109/itaic49862.2020.9339094.

Qian, Y., Jiacheng, R., Pengbo, W., Zhan, Y., & Changxing, G. (2020). Real-time detection and localization using SSD method for oyster mushroom picking robot. 2020 IEEE International Conference on Real-Time Computing and Robotics (RCAR).

https://doi.org/10.1109/rcar49640.2020.9303258.

Nithin, A., & Jaisharma, K. (2022). A deep learning based novel approach for detection of face mask wearing using enhanced single shot detector (SSD) over convolutional neural network (CNN) with improved accuracy. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). https://doi.org/10.1109/icbats54253.2022.9759018

Xie, Y., Ding, L., Zhou, A., & Chen, G. (2019). An optimized face recognition for edge computing. 2019 IEEE 13th International Conference on ASIC (ASICON).

https://doi.org/10.1109/asicon47005.2019.8983596

Younis, A., Shixin, L., Jn, S., & Hai, Z. (2020). Real-time object detection using pre-trained deep learning models MobileNet-SSD. Proceedings of 2020 6th International Conference on Computing and Data Engineering.

https://doi.org/10.1145/3379247.3379264

Jin, L., & Liu, G. (2021). An approach on image processing of deep learning based on improved SSD. Symmetry, 13(3), 495. https://doi.org/10.3390/sym13030495

Yamashige, Y., & Aono, M. (2019). FPSSD7: Real-time object detection using 7 layers of convolution based on SSD. 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA). https://doi.org/10.1109/icaicta.2019.8904089

Younis, A., Shixin, L., Jn, S., & Hai, Z. (2020a). Real-time object detection using pre-trained deep learning models MobileNet-SSD. Proceedings of 2020 6th International Conference on Computing and Data Engineering. https://doi.org/10.1145/3379247.3379264

Journal, I. (2022). Survey on real time multiple object detection using MobileNet-SSD with opencv. Interantional Journal Of Scientific Research In Engineering And Management, 06(06).

https://doi.org/10.55041/ijsrem14322

Chen, J., & Zhu, Z. (2022). Real-time 3D object detection and recognition using a smartphone. Proceedings of the 2nd International Conference on Image Processing and Vision Engineering. https://doi.org/10.5220/0011060600003209

Hu, J., Wang, T., & Zhu, S. (2022). Multi-view aggregation for real-time accurate object detection of a moving camera. Journal of Real-Time Image Processing, 19(6),1169–1179. https://doi.org/10.1007/s11554-022-01253-9

Balaji, K., & Gowri, S. (2021). A real-time face mask detection using SSD and mobilenetv2. 2021 4th International Conference on Computing and Communications Technologies (ICCCT). https://doi.org/10.1109/iccct53315.2021.9711784

S, R. R., N, S. Y., R, V. K., Iyengar, S. S., & M, P. L. (2021). Real-Time Multi- View Face Recognition using Alignment-RMFRA. Social Science Research Network. https://doi.org/10.2139/ssrn.3833799

Hartiwi, Y., Rasywir, E., Pratama, Y., & Jusia, P. A. (2020). Sistem Manajemen kehadiran dengan Fitur Pengenalan Wajah dan GPS Menggunakan YOLO pada Platform Android. Jurnal Media Informatika Budidarma, 4(4), 1235–1242. https://doi.org/10.30865/mib.v4i4.2522

Jaini, N. I., Asri, E., & Nova, F. (2021). Sistem Manajemen Kehadiran Menggunakan Metode Face Recognition Berbasis Web. Jurnal Ilmiah Teknologi Sistem Informasi, 2(2), 48–55. https://doi.org/10.30630/jitsi.2.2.39




DOI: http://dx.doi.org/10.23960/jitet.v12i1.3912

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