Multi-Criteria Recommender System for Determining Risk Level of Covid-19

The coronavirus pandemic is a global disease outbreak causing countless loss of lives and also threatening the economic, social, religious, education, and other key sectors of nations. This highly infectious virus continues to spread rapidly and therefore, the need to develop innovative strategies and policies to curb the growing effects becomes very crucial. One significant approach is the introduction of lockdown measures, although this instrument is not completely dependable, due to possible adverse effects on societal activities. Prior to deployment, a number of criteria are taken into account, including demographic conditions, healthcare options, and Covid-19 case data. Depending on the influencing factors, a lockdown decision is typically made by assessing the different danger levels of a certain place. Consequently, this research propose a multi-criteria recommender system to determine the worth and risk of various regions, based on several constraints and databases. The model, which utilized the analytical network process (ANP) to discover interconnectedness and feedback, also included the weighting technique. In this study carried out in 27 districts and cities in West Java, Indonesia, 15% of the selected locations were categorized as high-risk levels. Meanwhile, 63% and 22% were associated with medium and low risk, respectively.