SISTEM PENDETEKSI WAJAH BERMASKER SECARA REAL TIME MENGGUNAKAN METODE CNN

Authors

  • Nurul Adhayanti Universitas Gunadarma
  • Fathan Triyanto Nugroho Universitas Gunadarma
  • Romdhoni Susiloatmadja Universitas Gunadarma

DOI:

https://doi.org/10.56127/juit.v2i1.475

Keywords:

cnn, detection, face, mask, system

Abstract

In this study, a masked face detection system using the Convolutional Neural Network (CNN) method was tested. System testing was carried out to determine the success of the CNN method on a masked face detection system in real time if the mouth and nose parts of the face are not covered or covered by a mask or by anything other than a mask. This research was divided into 3 stages, namely dataset training, face detection, and testing in real time with various facial positions. This system successfully detects whether a face is masked or not in the dataset model with an accuracy of 99%. In real time, this system has succeeded in detecting well at various positions of facial appearance and on faces that are covered by a mask or hand on the nose and mouth.

References

Ahmad, F. L., Nugroho, A. & Suni, A. F. (2021). “Deteksi pemakai masker menggunakan metode haar cascade sebagai pencegahaan covid 19”. Edu Elektrika Journal, 10(1), pp. 13-18

Arafat, Ratna, S., & Wagino. (2022). “Deteksi masker wajah secara real time dengan arduino untuk mencegah penyebaran Covid-19”. Technologia, 13(4), pp. 375-383

Budiman, B., Lubis, C. & Perdana, N. J. (2021). “Pendeteksian penggunaan masker wajah dengan metode convolutional neural network”. Jurnal Ilmu Komputer dan Sistem Informasi, 9(1), pp. 40-47

Darmatasia. (2020). “Deteksi penggunaan masker menggunakan xception transfer learning”. Jurnal INSTEK: Informatika Sains dan Teknologi, 5(2), pp. 179-288

Dharmaputra, A., Cahyanti, M., Septian, M. R. D. & Swedia, E. R. (2021). “Aplikasi face mask detection menggunakan neural network mobilenetv2 berbasis android”. Sebatik, 25(2), pp. 383-389

Dores, V. (2022). “Identifikasi masker pada face detection dengan menggunakan metode Haar Cascade dan CNN”. Jurnal Sistim Informasi dan Teknologi, 4(4), pp. 149-154

Giancini, D., Puspaningrum, E. Y. & Via, Y. V. (2020). “Identifikasi penggunaan masker menggunakan algoritma CNN YOLOv3-Tiny”, Prosiding Seminar Nasional Informatika Bela Negara (SANTIKA), pp. 153-159

Hadiprakoso, R. B. & Qomariasih, N. (2022). “Deteksi masker wajah menggunakan deep transfer learning dan augmentasi gambar”. JIKO (Jurnal Informatika dan Komputer), 5(1), pp. 12-18

Hapsari, Y., Hidayattullah, M. F., Humam, M. & Nishom, M. (2022). “Automatic face mask detector menggunakan algoritma viola and jones”. Jurnal Informatika: Jurnal Pengembangan IT (JPIT), 7(1), pp. 32-36

Naufal, M. F. & Kusuma, S. F. (2021). “Pendeteksi citra masker wajah menggunakan CNN dan Transfer Learning”. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 8(6), pp. 1293-1300

Payana, M. D., TB, D. R. Y., Musliyana, Z. & Wibawa, M. B. (2022). “Deteksi masker wajah menggunakan metode Convolutional Neural Network (CNN) meningkatkan nilai akurasi melalui arsitektur layer konvolusi”. Journal of Informatics and Computer Science, 8(1), pp. 30-35

Pradana, A. I., Abdullah, R. W. & Harsanto. (2022). “Deteksi ketepatan pengunaan masker wajah dengan algoritma CNN dan Haar Cascade”. Jurnal Teknik Informatika dan Sistem Informasi, 9(3), pp. 2305-2316.

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Published

2023-01-16

How to Cite

Nurul Adhayanti, Fathan Triyanto Nugroho, & Romdhoni Susiloatmadja. (2023). SISTEM PENDETEKSI WAJAH BERMASKER SECARA REAL TIME MENGGUNAKAN METODE CNN. Jurnal Ilmiah Teknik, 2(1), 29–34. https://doi.org/10.56127/juit.v2i1.475

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