Klasifikasi Penggunaan Masker dengan Convolutional Neural Network Menggunakan Arsitektur MobileNetv2

Ihsan Mudzakir, Toni Arifin

Abstract


In the current pandemic situation caused by the COVID-19 virus. The spread of the virus comes from droplets or splashes attached to objects or public facilities. The use of masks is an effort to minimize the spread of this virus. The use of masks is required when carrying out activities in public places that allow spread. However, there are still people who are ignorant of this, so there must be officers who carry out supervision to bring order to the community so that they always wear masks. Classification by using Deep Learning to detect the use of masks is one way to help bring order to the community and assist officers in conducting surveillance. In this research, the Deep Learning model is made using the Convolutional Neural Network method and the MobileNetv2 architecture. The selection of this method is based on the effectiveness of the method in resource utilization that is not too heavy and can produce maximum accuracy. The dataset used is sourced from an open. The results of the model test for real-time mask detection succeeded in detecting objects in 2 classes, namely, mask and without a mask, and displayed excellent and accurate results with an average value of 0.99 on precision, recall, f1-score, and support.

Keywords


Classification; Machine Learning; Convolutional Neural Network; MobileNetv2

Full Text:

PDF

References


K. N. Azizah, “Apa yang Dimaksud dengan Droplet? Ini Penjelasannya,” https://health.detik.com/, 2020. https://health.detik.com/berita-detikhealth/d-5091352/apa-yang-dimaksud-dengan-droplet-ini-penjelasannya

WHO, “Anjuran mengenai penggunaan masker dalam konteks Covid-19,” World Heal. Organ, pp. 1–17, 2020.

F. Istyanto and A. Maghfiroh, “Peran Mikronutrisi Sebagai Upaya Pencegah Covid-19,” J. Ilm. Permas J. Ilm. STIKES Kendal, vol. 11, pp. 1–10, 2021.

T. K. P. S. T. P. Covid-19, “Lindungi Sesama dari Penularan Covid-19 dengan Disiplin Pakai Masker,” https://covid19.go.id/, 2021. https://covid19.go.id/p/berita/lindungi-sesama-dari-penularan-covid-19-dengan-disiplin-pakai-masker

M. Z. Asy’ari, “Apa itu tensorflow? 3 Hal Penting Untuk Dipahami,” https://auftechnique.com/, 2020. https://auftechnique.com/apa-itu-tensorflow/

T. Arifin, “Optimasi Decision Tree Menggunakan Particle Swarm Optimization untuk klasifikasi sel Pap Smear,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 3, pp. 572–579, 2020, doi: 10.35957/jatisi.v7i3.361.

T. Arifin, D. Riana, and G. I. Hapsari, “Klasifikasi Statistikal Tekstur Sel Pap Smear Dengan Decesion Tree,” J. Inform., vol. 1, no. 1, 2014, doi: 10.31311/ji.v1i1.180.

H. Mulyawan, M. Z. H. Samsono, and Setiawardhana, “Identifikasi Dan Tracking Objek Berbasis Image,” pp. 1–5, 2011, [Online]. Available: http://repo.pens.ac.id/1324/1/Paper_TA_MBAH.pdf

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, doi: 10.35585/inspir.v10i2.2569.

P. Arfienda, “Materi Pendamping Memahami Convolutional Neural Networks dengan Tensorflow,” https://algorit.ma/, 2019. https://algorit.ma/blog/convolutional-neural-networks-tensorfflow/

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, doi: 10.23917/emitor.v18i01.6236.

N. H. A.E. and M. I. Zul, “Aplikasi Penerjemah Bahasa Isyarat Indonesia Menjadi Suara Berbasis Android Menggunakan Tensorflow,” J. Komput. Terap., vol. 7, no. 1, pp. 74–83, 2021, [Online]. Available: https://iopscience.iop.org/article/10.1088/1757-899X/732/1/012082

R. O. Ekoputris, “MobileNet: Deteksi Objek pada Platform Mobile,” https://medium.com/, 2018. https://medium.com/nodeflux/mobilenet-deteksi-objek-pada-platform-mobile-bbbf3806e4b3

D. G. Arwindo, E. Y. Puspaningrum, and Y. V. Via, “Identifikasi Penggunaan Masker Menggunakan Algoritma CNN YOLOv3-Tiny,” Pros. Semin. Nas. Inform. Bela Negara, vol. 1, pp. 153–159, 2020, doi: 10.33005/santika.v1i0.41.

T. Septiana, N. Puspita, M. Al Fikih, and N. Setyawan, “Face Mask Detection Covid-19 Using Convolutional Neural Network ( CNN ),” Semin. Nas. Teknol. dan Rekayasa 2020, pp. 27–32, 2020.

M. M. Lambacing and F. Ferdiansyah, “Rancang Bangun New Normal Covid-19 Masker Detektor Dengan Notifikasi Telegram Berbasis Internet of Things,” Dinamik, vol. 25, no. 2, pp. 77–84, 2020, doi: 10.35315/dinamik.v25i2.8070.

A. Wikarta, A. S. Pramono, and J. B. Ariatedja, “Analisa Bermacam Optimizer Pada Convolutional Neural Network Untuk Deteksi Pemakaian Masker,” Semin. Nas. Inform. 2020 (SEMNASIF 2020), vol. 2020, no. Semnasif, pp. 69–72, 2020.

M. Inamdar and N. Mehendale, “ep rin t n ot Pr pe er r ie we d Pr rin t n ot ep pe er we”.

C. Dawson, A practical Guide to Research Methods: A user-friendly manual for mastering research techniquies and projects, 2nd editio. How to Books, 2006.

P. Bhandari, “Dataset,” https://github.com/, 2020. https://github.com/prajnasb/observations/tree/master/experiements/




DOI: http://dx.doi.org/10.36448/expert.v12i1.2466

Refbacks

  • There are currently no refbacks.


EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi

Published by Pusat Studi Teknologi Informasi, Fakultas Ilmu Komputer, Universitas Bandar Lampung
Gedung M Lt.2 Pascasarjana Universitas Bandar Lampung
Jln Zainal Abidin Pagaralam No.89 Gedong Meneng, Rajabasa, Bandar Lampung,
LAMPUNG, INDONESIA

Indexed by:



Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.