COMPARISON OF LINEAR AND VECTOR DATA NORMALIZATION TECHNIQUES IN DECISION MAKING FOR LEARNING QUOTA ASSISTANCE

Data normalization is essential for all kinds of decision-making problems, and a lot effort has been spent on the development models in multi-criteria decision making (MCDM), but despite this, there no definite answer to question: Which most appropriate technique?. This paper compares popular techniques: Linear Normalization (LN) Vector (VN) using VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) Method. The beneficiaries dataset learning quota was collected 399 students sample through observation (drive-test measurements online questionnaires) obtain information criteria data including attributes during Covid-19 pandemic. ranking results vector vs linear show how affected. difference selection best alternative (rank) shows that are differences assessments influenced by max-min criterion value which an impact rank- sum (benefit/cost). test clearly important it use (normalized) representation model because will often be where "the higher better" while others (cost) lower better".