Naive Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients - Lusiana Dwi Lestari; Iqbal Harifal; Taslim Taslim; Yogi Yunefri; Susi Handayani; Eka Sabna; Kursiah Warti Ningsih (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2022)
Covid-19 is a global pandemic that requires a coordinated worldwide response across all national health and healthcare systems. Identifying patients who are at high risk of contracting the Covid-19 virus is important to increase awareness before patients are further infected by the Covid-19 virus which can cause severe respiratory illness that requires special treatment in intensive care units (ICU). This study aims to predict ICU needs in patients infected with the Covid-19 virus. The value results from the prediction of ICU needs are used as a reference for hospitals to meet ICU needs for patients infected with Covid-19 so that they can increase ICU supplies. The prediction will be carried out using the Naïve Bayes algorithm method with optimization using the PSO algorithm. Based on the results of the study, the population size 20 with an accuracy value of the NBC algorithm was 87.03%, population size 40 with an accuracy value of 87.28, population size 60 obtained an accuracy of 87.13%, population size 80 with an accuracy value of 87.16 % and population size 100, the results obtained are 87.26% so that each population has an increase in the accuracy value.