Clustering K Means for Criteria Weighting With Improvement Result of Alternative Decisions Using SAW and TOPSIS

Daniati, Erna (2020) Clustering K Means for Criteria Weighting With Improvement Result of Alternative Decisions Using SAW and TOPSIS. In: 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 20-21 November 2019, Yogyakarta, Indonesia.

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Abstract

In today's world, decision making can be helped by using decision support systems. This system is an approach to support decision making. Decision makers use relative weights for each attribute. The obtained total score is the score of each alternative decision. The process of making alternative decision uses a combination of SAW and TOPSIS methods. In addition, the determination of criteria weight is also influenced by K Means method. This clustering method serves to provide an alternative weighting value so that decision makers no longer need to give the initialization value. The combination of these methods generate a more absolute alternative value than only using SAW. This research is a development of previous research by adding TOPSIS method. The resulting alternative decision has a more absolute and significant alternative value. This is indicated by comparison of TOPSIS usage results and using only SAW. This research uses data student of information system in Universitas Nusantara PGRI Kediri. It aims to proof the significant result for comparing with further method

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