RingkasanTesis KLASIFIKASI FACE MASK DETECTION MENGGUNAKAN VGG-16 DENGAN TRANSFER LEARNING

RingkasanTesis KLASIFIKASI FACE MASK DETECTION MENGGUNAKAN VGG-16 DENGAN TRANSFER LEARNING

• By Herliawan, Irwan;
• Tesis
• 410

Dunia saat ini sedang mengalami krisis kesehatan karena penyebaran penyakit Corona Virus Disease 2019 (Covid-19). Setiap harinya terjadi peningkatan data yang terpapar Covid-19 termasuk di Indonesia. Jika tidak secepatnya diatasi, hal ini akan mengakibatkan ketidakstabilan perekonomian di sebuah Negara. Virus ini menyerang anggota tubuh yaitu pada saluran pernapasan. Sesuai anjuran dari World Health Organization (WHO), salah satu perlindungan yang efektif adalah memakai masker ketika berada di kantor, sekolah, tempat belanja, rumah sakit dan tempat umum lainnya. Dalam penelitian ini bertujuan untuk membuat model deep learning pada facemask detection dataset menggunakan arsitektur VGG 16 dengan transfer learning. Model tersebut juga diuji dengan tiga optimizer yaitu Root Mean Square Propagation (RMSprop), Adaptive Moment Estimation (Adam) dan Stochastic Gradient Descent (SGD) untuk mengetahui model optimizer mana yang bekerja dengan optimal. Dari hasil penelitian ini, dapat disimpulkan bahwa model VGG 16 dengan transfer learning menggunakan optimizer RMSprop mencapai akurasi tertinggi yaitu 99%. Model ini dapat di implementasikan di area gedung untuk mendeteksi penggunaan masker, sehingga orang yang terdekat atau pihak keamanan dapat menegur orang yang tidak mematuhi peraturan tersebut.

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REFERENSI

[1] who.int, “Jumlah Perkembanagan Covid-19 di Dunia Menurut WHO,”
https://covid19.who.int/ diakses pada 29 Juni 2021 Pukul 22.32, 2021. .
[2] “Data Sebaran Covid-19 di Indonesia,” Https://covid19.go.id/ diakses pada 29 Juni 2021 Pukul 22.32, 2021. .
[3] C. I. Burhanuddin and M. N. Abdi, “Ancaman Krisis Ekonomi Global Dari Dampak Penyebaran Virus Corona (Covid-19),” AkMen, vol. 17, pp. 90– 98, 2020.
[4] A. Oumina, N. El Makhfi, and M. Hamdi, “Control the COVID-19 Pandemic: Face Mask Detection Using Transfer Learning,” 2020 IEEE 2nd Int. Conf. Electron. Control. Optim. Comput. Sci. ICECOCS 2020, 2020.
[5] D. M. Altmann, D. C. Douek, and R. J. Boyton, “What policy makers need to know about COVID-19 protective immunity,” Lancet, vol. 395, no. 10236, pp. 1527–1529, 2020.
[6] S. E. Snyder and G. Husari, “Thor: A deep learning approach for face mask detection to prevent the COVID-19 pandemic,” Conf. Proc. - IEEE SOUTHEASTCON, vol. 2021-March, 2021.
[7] M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,” Meas. J. Int. Meas. Confed., vol. 167, no. July 2020, p. 108288, 2021.
[8] A. Rahim, K. Kusrini, and E. T. Luthfi, “Convolutional Neural Network untuk Kalasifikasi Penggunaan Masker,” Inspir. J. Teknol. Inf. dan Komun., vol. 10, no. 2, p. 109, 2020.
[9] Darmatasia, “Analisis Perbandingan Performa Model Deep Learning untuk Mendeteksi Penggunaan Masker,” J. IT, vol. 11, no. 2, pp. 101–107, 2020.
[10] R. Kanotra, A. Analyst, N. Wadhwa, and N. Jeyanthi*, “Comparative Analysis of Object Detection Algorithms for Face Mask Detection,” Int. J. Eng. Adv. Technol., vol. 10, no. 4, pp. 148–151, 2021.
[11] T. Q. Vinh and N. T. N. Anh, “Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier,” Proc. - 2020 Int. Conf. Adv. Comput. Appl. ACOMP 2020, pp. 146–149, 2020.
[12] M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection,” Sustain. Cities Soc., vol. 65, no. November 2020, p. 102600, 2021.
[13] Darmasita, “Deteksi Penggunaan Masker Menggunakan Xception Transfer Learning,” J. INSTEK (Informatika Sains dan Teknol., vol. 5, pp. 279–288, 2020.
[14] K. R. Prilianti, T. H. P. Brotosudarmo, S. Anam, and A. Suryanto, “Performance comparison of the convolutional neural network optimizer for photosynthetic pigments prediction on plant digital image,” AIP Conf. Proc., vol. 2084, 2019.
[15] “Kaggle.com,” 2021. [Online]. Available: https://www.kaggle.com/.
[16] “Google Collaboratory,” 2021. [Online]. Available: https://colab.research.google.com/.[17] “Kaggle.com dataset,” 2021. [Online]. Available: https://www.kaggle.com/spandanpatnaik09/face-mask-detectormask-not- mask-incorrect-mask. [Accessed: 27-Apr-2021].
[18] “Training Dataset,” 2020. [Online]. Available: https://www.techopedia.com/definition/33181/training-data, 2020.
[19] I. Herliawan et al., “Classification of Liver Disease By Applying Random Forest,” vol. 6, no. 1, pp. 89–94, 2020.
[20] J. Chen, D. Lopresti, and G. Nagy, “Conservative preprocessing of document images,” Int. J. Doc. Anal. Recognit., vol. 19, no. 4, pp. 321– 333, 2016.
[21] E. S. Wijaya and Y. Prayudi, “Integrasi Metode Steganografi DCS Pada Image Dengan Kriptografi Blowfish Sebagai Model Anti Forensik Untuk Keamanan Ganda Konten Digital,” SNATI (Seminar Nas. Apl. Teknol. Informasi), pp. 11–17, 2015.
[22] E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2020-June, pp. 3008–3017, 2020.
[23] L. Fei-Fei, J. Deng, and K. Li, “ImageNet: Constructing a large-scale image database,” J. Vis., vol. 9, no. 8, pp. 1037–1037, 2010.
[24] Sujitpal, “Transfer learning and fne tuning for cross domain image classifcation with Keras. GitHub: transfer learning and fne tuning for cross domain image classifcation with Keras,” 2021. [Online]. Available: https://github.com/sujitpal/fttl-with-keras.
[25] D. Theckedath and R. R. Sedamkar, “Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks,” SN Comput. Sci., vol. 1, no. 2, pp. 1–7, 2020.
[26] R. D. Nurfita and G. Ariyanto, “Implementasi Deep Learning Berbasis Tensorflow Untuk Pengenalan Sidik Jari,” Emit. J. Tek. Elektro, vol. 18, no. 01, pp. 22–27, 2018.
[27] R. D. Nurfita and G. Ariyanto, “Implementasi Deep Learning Berbasis Tensorflow Untuk Pengenalan Sidik Jari,” Emit. J. Tek. Elektro, vol. 18, no. 1, pp. 22–27, 2018.
[28] S. Albawi, T. A. M. Mohammed, and S. Alzawi, “Layers of a Convolutional Neural Network,” Ieee, 2017.
[29] K. Yan, S. Huang, Y. Song, W. Liu, and N. Fan, “Face recognition based on convolution neural network,” Chinese Control Conf. CCC, pp. 4077– 4081, 2017.
[30] S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.
[31] S. Sharma, S. Sharma, and A. Athaiya, “Activation Functions in Neural Networks,” Int. J. Eng. Appl. Sci. Technol., vol. 04, no. 12, pp. 310–316, 2020.
[32] S. Khedkar, P. Gandhi, G. Shinde, and V. Subramanian, Deep Learning Techniques for Biomedical and Health Informatics. 2020.
[33] P. Toulis, T. Horel, and E. M. Airoldi, “The proximal Robbins–Monromethod,” J. R. Stat. Soc. Ser. B Stat. Methodol., vol. 83, no. 1, pp. 188–212, 2021.
[34] D. R. Ente, S. A. Thamrin, S. Arifin, H. Kuswanto, and A. Andreza, “Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus Di Rumah Sakit Unhas Menggunakan Algoritma C4.5,” Indones. J. Stat. Its Appl., vol. 4, no. 1, pp. 80–88, 2020.
[35] M. Yusa and W. Sindu, “Evaluasimodel Decision Tree C4.5 Guna Prediksi Posibilitas Resiko Obesitas,” Semin. Nas., pp. 147–152, 2015.
[36] N. Alfianika, Buku Ajar Metode Penelitian Pengajaran Bahasa Indonesia. Yogyakarta: Deepublish, 2018.
[37] M. M. Boulos, “Montclair State University Digital Commons Facial Recognition and Face Mask Detection Using Machine Learning Techniques,” 2021.
[38] A. Gholamy, V. Kreinovich, and O. Kosheleva, “Why 70/30 or 80/20 Relation Between Training and Testing Sets : A Pedagogical Explanation,” Dep. Tech. Reports, pp. 1–6, 2018.
[39] “Google Drive,” 2021. [Online].Available: https://www.google.com/drive/.