Rental house selection using Fuzzy TOPSIS

Mahmud, Muhamad Firdaus Madani and Zainol Abidin, Siti Nazifah and Mohd Jamil, Nor Hilaliyah and Mahmud, Amirah Nadhirah and Mohd Rohaizad, Nurina Izzati (2025) Rental house selection using Fuzzy TOPSIS. In: International Undergraduate Research, Innovation, Invention and Design (I-URIID) 2025: e-Book of Extended Abstracts. Universiti Teknologi MARA, Negeri Sembilan, pp. 221-225. ISBN 9786299595366
Abstract

The increasing number of students living off-campus necessitates effective decision-making tools for selecting suitable rental housing. Students frequently encounter challenges in identifying the most crucial criteria and choosing among numerous available options, as their decisions are heavily influenced by a multitude of factors. While various methods exist for multi-criteria decision-making, no prior research has specifically applied the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method to address the problem of optimal rental house selection for students. This study bridges this significant methodological gap by establishing a novel framework for determining the most preferred rental house among students. Key input criteria, including accessibility, facilities, cost, privacy, and environment, were identified as critical factors influencing student choices. The Fuzzy TOPSIS method was then systematically applied to rank various rental house alternatives, specifically Taman Seremban 3, Taman Rasah Jaya, Taman Iringan Bayu, and Taman Desa Rasah based on these weighted criteria. The findings of this study demonstrate that Taman Seremban 3 emerged as the most preferred rental house among students of UiTM Cawangan Negeri Sembilan Kampus Seremban 3. This result confirms the efficacy of Fuzzy TOPSIS in successfully assisting the complex selection process of student rental housing. This pioneering application of Fuzzy TOPSIS provides a robust and objective decision-making tool, with future potential for developing a dedicated system for diverse selection problems and for integration with other weighting methods to further enhance its applicability across various fields.

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