Face Mask Detection in the Covid-19 Pandemic Era by Implementing Convolutional Neural Network and Pre-Trained CNN Models

The Coronavirus or Covid-19 has spread widely throughout the world since the beginning of 2020. WHO provides basic guidance in preventing the spread of the virus that can be done by the community. One of them is the use of masks when doing activities outside the home. Lack of awareness in mask usage become the obstacle in the process of efforts to prevent the spread of covid 19. The aim of this research is to develop a face mask detection model by implementing the convolutional neural network and pre trained CNN algorithm. The accuracy of the proposed models in training process, the accuracy of CNN, VGG16, and VGG19 are 97.79%, 99.87% and 100%, respectively. The proposed models evaluated using confusion matrix using testing datasets given. © 2021 IEEE.

Prediction of COVID-19 Spreading Using Support Vector Regression and Susceptible Infectious Recovered Model

Many COVID-19 spread predictions have been implemented using various method. However, most of the prediction are missed because of many factors influence the COVID-19, e.g. geographic condition, socio-economic, government policy, etc. To handle this problem, the scenario-based prediction is proposed in this study to predict COVID-19 spread in Indonesia. This study proposed two methods to be used, i.e. Support Vector Regression (SVR) and Susceptible-Infectious-Recovered (SIR) Model. The prediction run for best-case scenario and worst-case scenario. Whereas best-case scenario used current daily case as a maximum case, worst-case scenario used another country's maximum case, i.e. India. SVR regression showed different end of epidemic, whereas best-case scenario on 21 January 2021, the worst-case scenario on 5 March 2021. SIR-Model showed the similar end of epidemic on January 2021 for both scenarios but showed the dramatically increase of infectious people from 450,000 people in best-case scenario to 5,500,000 people in worst-case scenario. The prediction can be used as an insight for the policy maker in combating the COVID-19 pandemic. © 2020 IEEE.

Local Binary Pattern Histogram for Face Recognition in Student Attendance System

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy. © 2020 IEEE.