Multi-Criteria Prediction Framework for the Prioritization of Council Candidates based on Integrated AHP-Consensus and TOPSIS Methods

Predicting the council candidate becomes difficult due to the large number of criteria that must be known and identified. The best candidate should be chosen from among the candidates because he or she will play an important role in the organization or institution. It is critical to find the right and best candidate these days because people see and judge the outcome from the candidate in a short time with the help of social media. Perhaps the organization and institution require the best candidate criteria because they will manage and organize the community around them. This study focuses on how to prioritize council candidates using Analytic Hierarchy Process (AHP) for determine the criteria and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for prioritize the student council candidate. This proposed framework based on Multi-Criteria Decision Making (MCDM) will be used to recommend and assist students in selecting the best candidate for student council. The three criteria chosen were grade point average (GPA), Age, and Semester. Based on the results of the questionnaire and a review of the literature, these criteria were developed. The three criteria were then used to determine the most important criterion for selecting the student council. The AHP weight is used to determine and prioritize the most important criteria. TOPSIS was used to select the most qualified student council candidate. The findings show that GPA is the most important criteria in selecting the best candidate, and the TOPSIS findings support the AHP findings.

@article{Zulkefli2022,
  title     = {Multi-Criteria Prediction Framework for the Prioritization of Council Candidates based on Integrated AHP-Consensus and TOPSIS Methods},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Nurul Akhmal Mohd Zulkefli and Muhamad Hariz Muhamad Adnan and Mukesh Madanan and Tariq Mohsen Hardan},
  doi       = {10.14569/IJACSA.2022.0130241},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130241}
}