Normalized Data Technique Performance for Covid-19 Social Assistance Decision Making

Abstract

The student internet data assistance program is an effort by educational institutions to support online learning from home during the Covid-19 pandemic. A series of tests are applied to determine the optimization of decision making on the social assistance program performance. This study aims to evaluate the performance of students' internet data assistance programs using a confusion matrix approach, in particular on the performance of simple, linear and vector normalized data analysis techniques. The representation normalized techniques performance for simple data using SAW, linear data is VIKOR and vector using the MOORA method. The study results found that there were differences in performance in the process of selecting preferences for ranking potential social assistance recipients, as well as a differential in the confusion matrix performance values on the accuracy, precision, recall and error rate values on each method.