Masked Face Detection System Using Convolutional Neural Networks

The global health epidemic caused by the breakout of a coronavirus disease in 2019 (COVID-19) has had a significant impact on the way we view our environment and live our daily lives. People should wear masks and maintain social distance as a precaution. As a result, detecting face masks has become a critical responsibility in assisting the global community. This report describes a simplified method for accomplishing this goal by utilising TensorFlow, Keras, OpenCV, Convolutional Neural Networks, and some basic Machine Learning packages. If a person is discovered without a face mask, an alert warning is issued, and the person's face is captured. In addition, the value of mask and unmask faces are saved in the cloud for future use. Using this deep learning enables the system to be faster and more precise to detect the faces, and as a result, the accuracy of mask and unmask faces detection is higher than 90%. As all the facilities and premises open and the number of COVID-19 cases continues to rise across the country, everyone must adhere to the safety precautions until the outbreak is over. As a result, this module assists in recognising people wearing masks when entering premises. © 2022 IEEE.

IoT-based Micro-Hydroponic Urban Farming for Economic Healing during COVID19 Pandemic

The COVID-19 pandemic that has lasted more than a year has brought down the family's economy. Activity restrictions imposed by the government to suppress the increase in the number of sufferers resulted in the cessation of community economic activities, especially in the informal, non-essential and non-critical sectors. The work from home regulation allows economic activities by utilizing the limited land around the house. Hydroponic farming is an alternative activity because it is easy and does not require enormous costs. This study develops a micro-hydroponic system that is suitable for the limited availability of land in urban housing. Internet of things (IoT)-based control and monitoring system is applied to manage the hydroponic system from anywhere and anytime using a smartphone. Experiments using lettuce during one growing season show that this hydroponic system with IoT control can produce fresh and quality vegetables consumed by themselves to increase endurance. The sale of the harvest can provide an additional income of 225 thousand rupiahs for an area of one square meter. This extra income is expected to restore the economies of urban communities that have limited housing areas and work in the informal sector. © 2021 IEEE.

Document Search in Information Retrieval System Using Vector Space Model

The current pandemic has spread everywhere. Various effects of pressure in the economic, educational, and social sectors are forced to adjust. So that people really need information about efforts to prevent the spread is very necessary. Search Engine is a program that is used as a tool to find more information on the internet. Search Engine is one of the discussions in the field of Information Retrieval. This system is a document search of unstructured properties. Thus, being able to provide the information needs of a large set of documents (on a local computer server or the internet). The vector space model is one of the many models in Information Retrieval that is used to get the distance and direction between keywords and documents by representing them into vectors. Then the results of ranking using cosine similarity with a dataset of 90 articles about covid19 along with 4 keywords will be tested with precision, recall, and accuracy calculations. The results of the precision calculation get a value of 60% - 73%, recall gets a value for each of the keywords 81%-100% and gets an accuracy value of 85% -89%. The results of these experiments indicate that information retrieval with vector space model is effective with good and stable performance used for information retrieval. © 2021 IEEE.

Development of the Personnel Monitoring System Using Mobile Application and Real-Time Database during the COVID19 Pandemic

Coronavirus disease 19 (COVID19) is a disease caused by the new coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has infected almost the entire world with a total of 47.5 million sufferers and a death toll of 1.2 million people so that WHO categorizes it as a global pandemic. The COVID19 case in Indonesia still shows an increasing trend even though various prevention efforts have been made. Proven efforts to reduce the spread of COVID19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home. Several government and private agencies have required employees to report their health conditions via web pages. Real-time and accurate mobile applications can help prevent the spread of COVID19. This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology. The application will collect GPS-based location data, the number of people in the vicinity identified via Bluetooth, and the user's body condition in the form of temperature and oxygen levels in the blood. User data is stored and processed in a real time database in cloud computing which can be accessed through an application on the user's smartphone. The database also stores data on Covid19 sufferers and where they live. The application provides alerts when in a crowd and notifies the status of the region the user is in. Advice is given by the app when the recording of the body condition points to the early symptoms of COVID19. © 2020 IEEE.