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Soft Solution of Soft Set Theory for Recommendation in Decision Making

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Recent Advances on Soft Computing and Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 287))

Abstract

Soft set theory is a new general mathematical method for dealing with uncertain data which proposed by Molodtsov in 1999 had been applied by researchers in decision making problems. However, most existing studies generated exact solution that should be soft solution because the determination of the initial problem only uses values ​​or language approach. This paper shows the use of soft set theory as a generic mathematical tool to describe the objects in the form of information systems and evaluate using multidimensional scaling techniques to find the soft solution and recommendation for making a decision.

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Author information

Authors and Affiliations

  1. Department of Statistics, Universitas Islam Indonesia, Jalan Kaliurang KM 14, Yogyakarta, Indonesia

    R. B. Fajriya Hakim

  2. AMCS Research Center, Yogyakarta, Indonesia

    Eka Novita Sari

  3. Department of Information System, University of Malaya, 50603, Pantai Valley, Kuala Lumpur, Malaysia

    Tutut Herawan

Authors
  1. R. B. Fajriya Hakim
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  2. Eka Novita Sari
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  3. Tutut Herawan
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Corresponding author

Correspondence to R. B. Fajriya Hakim .

Editor information

Editors and Affiliations

  1. Department of Information System Faculty of Comp. Sci. & Info. Tech., University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Comp. Sci. and Info. Tech, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Rozaida Ghazali

  3. Faculty of Comp. Sci. and Info. Tech., Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Mustafa Mat Deris

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© 2014 Springer International Publishing Switzerland

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Hakim, R.B.F., Sari, E.N., Herawan, T. (2014). Soft Solution of Soft Set Theory for Recommendation in Decision Making. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-07692-8_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07691-1

  • Online ISBN: 978-3-319-07692-8

  • eBook Packages: EngineeringEngineering (R0)

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