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Comparison of MAUT Method with WASPAS Method in IBN Lecturer Performance Assessment

Authors: Adi Prasetia Nanda; Elisabet Yunaeti Anggraeni;

Comparison of MAUT Method with WASPAS Method in IBN Lecturer Performance Assessment

Abstract

Lecturers are educators in higher education institutions. The role of a lecturer is very influential on the quality of education in a university, both State Universities (bahasa: Perguruan Tinggi Negeri (PTN)) or Private Universities (bahasa: Perguruan Tinggi Swasta (PTS)). With the assessment of lecturer performance, certain assessments are created to be used as a benchmark for the ability/quality/value of a lecturer at a university. In this study, researchers compare the MAUT method and the WASPAS method which will provide an alternative answer that is more accurate while at the same time knowing which method is the best between the two methods, as well as to be used by universities in determining the most influential factors in determining the quality of a person's performance lecturer. Comparing the two methods will get an appropriate method in determining the performance of lecturers in a university.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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