Covid Classification System for Covid Detection

Abstract

The ongoing global pandemic, which has now become an endemic has had a significant impact on the educational sector. Despite advancements in technology, there is no real- time prevention of COVID-19 transmission especially UiTM Tapah students that must go through crowd to reach health unit and have high possibility of spreading the disease. This research aims to develop a mobile application by utilizing machine learning for UiTM Tapah students to classify their COVID-19 status. To ensure a systematic and efficient development process, the research adopted the Mobile Application Development Life Cycle (MADLC) methodology. Within the application, the core of the machine learning functionality lies in the implementation of an Artificial Neural Network (ANN). By using 5,434 samples of data that had previously been classified by previous studies that analyzed student data such as symptoms to identify potential cases of the virus. The ANN approach performed greatly with the accuracy of 98%. Feedback from 32 respondents helped identify students' difficulties during the pandemic, which results in majority of the respondents agreeing that MCO affecting them adapting to online learning, and access to healthcare facilities. Furthermore, the usability testing conducted using the System Usability Scale (SUS) provided valuable insights into the system's user-friendliness and effectiveness. The results indicated a high level of usability, with a SUS score of 88.3 from 30 UiTM Tapah students while 76.25 from UiTM Tapah lecturers. This system was usable to classify the risk of infection among students who had not yet been diagnosed, while having some room for improvements. The system's ability to identify infected individuals promptly and accurately aided in controlling the spread of the virus, thereby protecting both students and staff. Thus, the classification system by using ANN was a valuable tool for public health organizations in their efforts to combat the COVID-19 pandemic.