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Computer Science & IT Engineering

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Deteksi Masker Pencegahan Covid19 Menggunakan Convolutional Neural Network Berbasis Android Purnama Nyoman; Putu Kusuma Negara
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (589.814 KB) | DOI: 10.29207/resti.v5i3.3103

Abstract

Masks are an important part of preventing Covid19 disease.The World Health Organization (WHO) have also recommended the community use masks when doing activities in public areas. There are many types of masks that are used to cover the nose and mouth. In general, there are about 3 types of masks that are commonly used by the public today, namely medical masks, N95 and cloth masks. This study aims to detect the type of mask used by the community. So that it can make easier for the government to apply discipline in COVID-19 health protocol. The detection method used in this study is a convolutional neural network (CNN). The first step is acquisition of knowledge, which first collects the types of masks on the market, followed by the representation of that knowledge before being modeled into a mathematical calculation formula, which will then be processed using the Convolutional Neural Network method. The system will be carried out by analyzing the recall value, its precision and accuracy.Testing process is carried out on an Android-based device and the mobilenetV2 framework. In this study, the accuracy value is 90% using ADAM Optimization and 80 % using Gradient descent optimization.
Co-Authors Nyoman Purnama, Nyoman