DECISION SUPPORT SYSTEM FOR NATURAL DISASTER AID RECIPIENTS USING THE TOPSIS METHOD

(CASE STUDY: MAJENE DISTRICT REGIONAL DISASTER MANAGEMENT AGENCY OFFICE)

  • Yunita Hadrianti J.A(1*)
    Fakultas Teknik, Universitas Sulawesi Barat
  • Musyirifah Musyirifah(2)
    Fakultas Teknik, Universitas Sulawesi Barat
  • Farid Wajidi(3)
    Fakultas Teknik, Universitas Sulawesi Barat
  • (*) Corresponding Author
Keywords: Natural Disasters, Disaster Victims, Beneficiary, TOPSIS Method

Abstract

This research aims to produce a website-based system by implementing the TOPSIS method into the system to solve the problem of determining prospective recipients of natural disaster assistance. The type of research used is the type of development using the Waterfall model. The test subjects in this study were the Majene Regency Regional Disaster Management Agency staff. Data on the response of the staff of the Regional Disaster Management Agency Office regarding the implementation of the TOPSIS method in the decision support system for recipients of natural disaster assistance was collected through a questionnaire. The data collected in the trial was then analyzed using quantitative analysis techniques. The results of the research data show that (i) The results of the application of the TOPSIS method have succeeded in providing recommendations for determining prospective recipients of natural disaster assistance, (i) The results of testing with blackbox test show that this decision support system has no defects or errors, which means that the system created already meets the functional requirements, (ii) The User Acceptance Test (UAT) test produced 82% of users who stated that they strongly agreed with the existence of this decision support system which could assist in determining potential recipients of natural disaster assistance according to the criteria used.

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Published
2023-10-31
How to Cite
[1]
Y. J.A, M. Musyirifah, and F. Wajidi, “DECISION SUPPORT SYSTEM FOR NATURAL DISASTER AID RECIPIENTS USING THE TOPSIS METHOD”, jicon, vol. 11, no. 2, pp. 185-194, Oct. 2023.
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