PEMBUATAN APLIKASI PENGENALAN WAJAH UNTUK SISTEM PRESENSI KELAS MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK
Abstract Views:
19 times
PDF Downloads:
23 times
Abstract
Abstract—Attendance is a method used by an agency to record workers who work in an agency or students who are in an educational agency. During the pandemic, the University of Surabaya changed the attendance system it used, the system first used before the pandemic was paper, during the pandemic the University of Surabaya changed the attendance system to use a website. On this website, students only need to select courses that are currently in progress and carry out the attendance process. In 2022 the Indonesian government will allow universities and schools to carry out face-to-face learning processes. The University of Surabaya is also making a transition from online to face-to-face learning, however the attendance system used is still a website, this causes students to have fictitious attendance or make attendance but students do not attend class. Based on these problems, this research created an application that can help the university to prevent students from making fictitious attendance by using face recognition in the attendance process using the Convolutional Neural Network or CNN method. The process of creating a CNN model will use a pre-trained model, namely GoogleNet, which has 1 layer added and the Hyperparameter Tuning process will be used to get the best CNN model by looking for optimal Hyperparameter values based on the predetermined Hyperparameter values and types. One of the results of the CNN model making trial was that the best model was obtained with an accuracy rate of 97%.
Keywords: convolutional neural network, face recognition, attendance
Abstrak—Presensi merupakan sebuah metode yang digunakan oleh sebuah instansi untuk mencatat para pekerja yang bekerja di sebuah instansi tersebut atau para mahasiswa/i atau siswa/i yang berada di sebuah instansi pendidikan. Pada masa pandemi Universitas Surabaya mengubah sistem presensi yang digunakan, sistem yang pertama kali digunakan sebelum pandemi berupa kertas, saat pandemi Universitas Surabaya mengubah sistem presensi menggunakan website. Pada website ini mahasiswa hanya perlu memilih mata kuliah yang sedang berlangsung dan melakukan proses presensi. Pada tahun 2022 pemerintah Indonesia memperbolehkan Universitas serta Sekolah untuk melakukan proses pembelajaran secara tatap muka. Universitas Surabaya juga melakukan transisi dari pembelajaran online menjadi tatap muka, akan tetapi sistem presensi yang digunakan masih berupa website, hal ini menyebabkan mahasiswa presensi fiktif atau melakukan presensi namun mahasiswa tidak mengikuti kelas. Berdasarkan permasalahan tersebut pada penelitian ini dibuat sebuah aplikasi yang dapat membantu pihak universitas untuk mencegah mahasiswa melakukan presensi fiktif dengan digunakannya pengenalan wajah atau face recognition dalam proses presensi menggunakan metode Convolutional Neural Network atau CNN. Proses pembuatan model CNN akan digunakan model pre-trained yaitu GoogleNet yang ditambahkan 1 layer dan akan digunakan proses Hyperparameter Tuning untuk mendapatkan model CNN terbaik dengan mencari nilai Hyperparameter yang optimal berdasarkan nilai dan jenis Hyperparameter yang telah ditentukan. Salah satu hasil uji coba pembuatan model CNN didapatkan model terbaik dengan tingkat akurasi 97%.
Kata kunci: convolutional neural network, face recognition, presensi
Downloads
References
Adusumalli, H., Kalyani, D., Sri, R. K., Pratapteja, M., & Rao, P. V. R. D. P. (2021). Face Mask Detection Using OpenCV. Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 1304–1309. https://doi.org/10.1109/ICICV50876.2021.9388375
Bayat, O., Aljawarneh, S., Carlak, H. F., International Association of Researchers, Institute of Electrical and Electronics Engineers, & Akdeniz Üniversitesi. (2017). Proceedings of 2017 International Conference on Engineering & Technology (ICET’2017) : Akdeniz University, Antalya, Turkey, 21-23 August, 2017.
Cengil, E., Çınar, A., & Özbay, E. (2017, October). Image classification with caffe deep learning framework. In 2017 International Conference on Computer Science and Engineering (UBMK) (pp. 440-444). IEEE.
Endrianti, F., Setiawan, W., & Wihardi, Y. (2018). Sistem Pencatatan Kehadiran Otomatis di Ruang Kelas Berbasis Pengenalan Wajah Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Aplikasi dan Teori Ilmu Komputer, 1(1), 40-44.
Fitra Maulana, F., & Rochmawati, N. (2019). Klasifikasi Citra Buah Menggunakan Convolutional Neural Network.
Gonzalez, R. C., & Woods, R. E. (Richard E. (n.d.). Digital image processing.
Hartiwi, Y., Rasywir, E., Pratama, Y., Jusia, P. A., Dinamika, U., & Jambi, B. (2020). Eksperimen Pengenalan Wajah dengan fitur Indoor Positioning System menggunakan Algoritma CNN. 22(2). https://doi.org/10.31294/p.v21i2
Hartono, I., Noertjahyana, A., & Santoso, L. W. (2022). Deteksi Masker Wajah dengan Metode Convolutional Neural Network. Jurnal Infra, 10(1), 203-209.
Hosseini, M., Bani-Hani, D., & Lam, S. S. (2022). Leukocytes Image Classification Using Optimized Convolutional Neural Networks. Expert Systems with Applications, 205. https://doi.org/10.1016/j.eswa.2022.117672
Ihwan, A. (2013). Metode Jaringan Saraf Tiruan Propagasi Balik Untuk Estimasi Curah Hujan Bulanan di Ketapang Kalimantan Barat.
Jatmika, S., Aprilianto, T., & Idris, M. (2020). Positif : Jurnal Sistem dan Teknologi Informasi EKSTRAKSI FITUR UNTUK MENGIDENTIFIKASI MARGA TANAMAN MENGGUNAKAN ALGORITMA BACKPROPAGATION.
Jayaraman, U., Gupta, P., Gupta, S., Arora, G., & Tiwari, K. (2020). Recent development in face recognition. Neurocomputing, 408, 231–245. https://doi.org/10.1016/j.neucom.2019.08.110
Khilwani Ibrahim, N., Kasmuri, E., Jalil, N. A., Adili Norasikin, M., Salam, S., & Riduwan Md Nawawi, M. (2013). License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study. JSCSE], 3(3). https://doi.org/10.7321/jscse.v3.n3.15
Khunafa Qudsi, N., Asmara, R. A., Syulistyo, A. R., Studi, P., Informatika, T., Informasi, J. T., & Malang, P. N. (2019). Identifikasi Citra Tulisan Tangan Digital Menggunakan Convolutional Neural Network (CNN).
Maeda-Gutiérrez, V., Galván-Tejada, C. E., Zanella-Calzada, L. A., Celaya-Padilla, J. M., Galván-Tejada, J. I., Gamboa-Rosales, H., Luna-García, H., Magallanes- Quintanar, R., Guerrero
Méndez, C. A., & Olvera-Olvera, C. A. (2020). Comparison of convolutional neural network architectures for classification of tomato plant diseases. Applied Sciences (Switzerland), 10(4). https://doi.org/10.3390/app10041245
Mbunge, E., Simelane, S., Fashoto, S. G., Akinnuwesi, B., & Metfula, A. S. (2021). Application of deep learning and machine learning models to detect COVID-19 face masks - A review. In Sustainable Operations and Computers (Vol. 2, pp. 235–245). KeAi Communications Co. https://doi.org/10.1016/j.susoc.2021.08.001
Mehindra Prasmatio, R., Rahmat, B., & Yuniar, I. (2020b). ALGORITMA CONVOLUTIONAL NEURAL NETWORK. In Jurnal Informatika dan Sistem Informasi (JIFoSI) (Vol. 1, Issue 2).
O’Shea, K., & Nash, R. (2015). An Introduction to Convolutional Neural Networks. http://arxiv.org/abs/1511.08458
Pang, B., Nijkamp, E., & Wu, Y. N. (2020). Deep Learning With TensorFlow: A Review. In Journal of Educational and Behavioral Statistics (Vol. 45, Issue 2, pp. 227–248). SAGE Publications Inc. https://doi.org/10.3102/1076998619872761
Prasetya, A., Ihsanto, E., & Dani, A. W. (2021). Rancang Bangun Pendeteksi Wajah Bermasker Dan Tidak Bermasker Dalam Absensi Di Masa Pandemi COVID-19 Menggunakan Convolutional Neural Network. Jurnal Teknologi Elektro, 12(2), 80. https://doi.org/10.22441/jte.2021.v12i2.006
Satwikayana, S., Wibowo, S. A., & Vendyansyah, N. (2021). SISTEM PRESENSI MAHASISWA OTOMATIS PADA ZOOM MEETING MENGGUNAKAN FACE RECOGNITION DENGAN METODE CONVOLUTIONAL NEURAL NETWORK BERBASIS WEB. In Jurnal Mahasiswa Teknik Informatika) (Vol. 5, Issue 2).
Sharma, S., Sharma, S., & Athaiya, A. (2020). ACTIVATION FUNCTIONS IN NEURAL NETWORKS. In International Journal of Engineering Applied Sciences and Technology (Vol. 4). http://www.ijeast.com
Sumarni, Y. (2020). Pandemi Covid-19: Tantangan ekonomi dan bisnis. Al-Intaj: Jurnal Ekonomi Dan Perbankan Syariah, 6(2), 46-58.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going Deeper with Convolutions. http://arxiv.org/abs/1409.4842
Tan, X., Chen, S., Zhou, Z. H., & Zhang, F. (2006). Face recognition from a single image per person: A survey. Pattern recognition, 39(9), 1725-1745.
Umar, R., & Nurrahim, A. (2020). Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network. https://www.mathworks.com/discovery/convolutional-neural-network.html
Widiyono, A. (2020). Efektifitas Perkuliahan Daring (Online) pada Mahasiswa PGSD di Saat Pandemi Covid 19. Jurnal Pendidikan, 8(2).
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- Articles published in CALYPTRA are licensed under a Creative Commons Attribution-ShareAlike 4.0 International license. You are free to copy, transform, or redistribute articles for any lawful purpose in any medium, provided you give appropriate credit to the original author(s) and the journal, link to the license, indicate if changes were made, and redistribute any derivative work under the same license.
- Copyright on articles is retained by the respective author(s), without restrictions. A non-exclusive license is granted to CALYPTRA to publish the article and identify itself as its original publisher, along with the commercial right to include the article in a hardcopy issue for sale to libraries and individuals.
- By publishing in CALYPTRA, authors grant any third party the right to use their article to the extent provided by the Creative Commons Attribution-ShareAlike 4.0 International license.
