Multicriteria Recommender System: A New Model for Determining risk level of COVID-19 in Indonesia

Abstract

Coronavirus has spread all over the world and brought significant impact to everyday life. This highly infectious virus calls for new strategies and policies that are able to suppress its transmission rate. One of the ways is by lockdown. However, lockdown is not an infallible solution and it brings with it negative impacts on society in terms of socioeconomic and mental health aspects. In addition, several factors should also be taken into account before the implementation such as demographic conditions, health services and COVID-19 case data itself. In general, a lockdown decision can be determined by looking at the level of risk of an area. Naturally, this assessment of levels of risk must be based on parameters affecting the lockdown. In this paper, we propose a multicriteria recommender system model that can calculate the value and level of risk for each region by taking into account several parameters from different databases. This model is also equipped with the process of weighting using the Analytical Network Process (ANP) method to determine interdependence and feedback between parameters. From the experiment conducted on 27 cities and districts in West Java, it was found that 15% of the regions were in the high-risk category, 41% of the regions have medium risk and 44% of the regions have a low risk.

Recommendation System for Selecting Web Programming Learning Materials for Vocational High School Students using Multi-criteria Recommendation Systems

“…Recommendations can be based not only on titles but also on descriptions or abstracts [8]. During the COVID-19 pandemic, recommendation systems were used to calculate the risk levels of various regions by considering several criteria [9]. In local environments, recommendation systems are also useful, for example, in determining nearby communities based on location, category, and interests [10].…”