Optimizing Student Housing Search with MAUT-Based Dorm Finder Web System


    Andi Leo( 1 ) Imelda Linda Lestari( 2 ) Andri Wijaya( 3 ) Benny Daniawan( 4 )

    (1) Buddhi Dharma University
    (2) Buddhi Dharma University
    (3) Buddhi Dharma University
    (4) Buddhi Dharma University




Dorm Finder , MAUT, Optimizing Search, Student Housing, Web System


In response to the challenges faced by students in finding suitable temporary accommodations, this study introduces 'Dorm Finder,' a web-based information system utilizing Multi Attribute Utility Theory (MAUT). This innovative platform is designed to assist students in locating boarding houses that align with their preferences, taking into account amenities, location, and rental prices. MAUT allows for a nuanced and personalized decision-making process, enabling students to assign importance to various attributes, simplifying their search. The study also addresses the growing demand for efficient housing solutions, as the number of university students has increased by 4.02% in 2022. The research extends to evaluating the system's impact, aiming to support students in their search for accommodations that meet their specific needs based on key attributes. This evaluation will inform the development of more effective MAUT-based systems in the future. The use of MAUT in this context, with its demonstrated effectiveness in decision-making processes, reinforces the potential efficacy of the Dorm Finder system. The 'Dorm Finder' system offers a tailored platform that provides comprehensive information and customizes the search experience for individual student preferences. The system's design and implementation ensure an intuitive and efficient user experience through strategic interface design, including color selection, layout, and interactivity, facilitating a straightforward and enjoyable user journey. The system’s potential to revolutionize the way students locate and select accommodations is a significant contribution to the digitalization of the housing search sector.


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How to Cite

A. Leo, I. L. Lestari, A. Wijaya, and B. Daniawan, “Optimizing Student Housing Search with MAUT-Based Dorm Finder Web System”, bit-Tech, vol. 6, no. 2, pp. 223–234, Dec. 2023.



DOI : https://doi.org/10.32877/bt.v6i2.1053
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