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All Journal Riau Journal of Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Informatika dan Rekayasa Perangkat Lunak REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Abdi Dharma
Universitas Prima Indonesia
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Comparative Analysis of Convolutional Neural Network Methods in Detecting Mask Wear Handoko Handoko; Fahrul Rozy; Felix Elbert Gani; Abdi Dharma
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 5, No 2 (2022): Budapest International Research and Critics Institute May
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i2.5605

Abstract

Covid19 is a disease caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). As a result, the respiratory system becomes disrupted. The spread of this disease is very easy through droplets. The use of masks is one way to prevent being attacked by this virus. The system for detecting the use of masks is very necessary for today in detecting whether someone is wearing a mask. Convolutional Neural Network (CNN) is a method that can be used to detect masks. In this study, the VGG16, Resnet50, and MobileNet models will be used. Before conducting data training, data preprocessing and data augmentation were carried out on the dataset. The test accuracy of the VGG16, Resnet50, and MobileNet models are 96% and 96% and 98%, respectively. From the test results, it is found that the MobileNet model is more appropriate in the case of mask detection. The conclusion obtained is the use of the MobileNet architecture, the resources used for classifying can be reduced compared to other architectures. MobileNet uses the Depth-Wise Separable method in the computing process which reduces the computational process.
Co-Authors A. Henpra Yogi Sitanggang Aditya Calderon Naibaho Alhoi Andrew Jefferson Alvin Alvin Andika Dian Permana Andrian Bodhi Lhoardi Christensen Christensen Darwis Darwis Dinda Afrianti Elvandric Lase Endy Pranoto Eric Wibowo FAHRUL ROZY Fangatulo Dodo Telaumbanua Felix Elbert Gani Frenky Lutan Frenky Tanoto Handoko Handoko Hartato Bindan Hendra Handoko Syahputra Pasaribu, Hendra Handoko Johanes Pardede Joseph Siahaan Joshua Baen Tupa Lolo Mulatua Bancin Lymin Lymin Magdalena Magdalena Michael Michael Muhammad Uwais Akbar Peringatan Hulu Rikky Romeo Lumbantong Salim, Edward Suwandy Sibarani, Lelawati Steele Sumita Wardani Togar Zulfiter Nadeak Wonderson Wonderson