Komparasi Kinerja ELECTRE dan MOORA dalam Menentukan Konsentrasi Tingkat Kesuburan Sperma
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Abstract
The aim of this research is to test and compare the performance of the ELECTRE method with MOORA in decision making. By using the attribute weighting process, namely the calculation of Information Gain in both methods, the attribute weights are obtained systematically and objectively, therefore they are no longer determined by the assumptions of decision makers. The test data instrument used is a dataset from the UCI Machine Learning Repository, namely the Fertility Dataset which is data on the level of sperm fertility concentration with 100 data records, 9 attributes, 1 class variable and the data set is multivariate. The results of testing the ELECTRE and MOORA methods in this study indicate that the two methods have differences in the results of ranking the best alternative for sperm fertility concentration levels. The ELECTRE method produces A2 as the best alternative, while the MOORA method produces A16 as the best alternative. Then in terms of program execution time, the MOORA method is faster, namely 0.02 seconds, while the ELECTRE method execution time is 1.88 seconds.
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