Application of the ANFIS Method to Predict Satisfaction with Facilities and Infrastructure

Authors

  • Mardi Turnip Universitas Prima Indonesia
  • Ganang Reza Priambodo Universitas Prima Indonesia
  • Theresia Delima Sihaloho Universitas Prima Indonesia
  • Jonathan Haris P. Ndruru Universitas Prima Indonesia
  • Josepta Sigalingging Universitas Prima Indonesia
  • Salsabillah Universitas Prima Indonesia
  • Haposan Daniel Panjaitan Universitas Prima Indonesia

DOI:

https://doi.org/10.59934/jaiea.v3i1.283

Keywords:

ANFIS, Application, Facilities and infrastructure, Satisfication

Abstract

Facilities and infrastructure are all movable or immovable objects or objects that are used to support every aspect of human life. Students, lecturers and office workers at least spend about half of their active hours at work. Therefore it is very important to pay attention to the high level of comfort, security, completeness in a building. There fore we need a way to predict satisfaction with facilities and infrastructure. To provide solutions to existing problems, the authors create applications that can predict the satisfaction of facilities and infrastructure. In this article, a satisfaction prediction approach based on a data-driven technique, representing system behavior using the Takagi-Sugeno model is developed. The Adaptive Neuro Fuzzy Inference System method is used to build a predictive model. The research was conducted by interview, observation and literature study. Data were taken from 92 respondents consisting of lecturers, students, and staff/employees in the research area. The test results using this method showed satisfactory results, indicating a success rate with an accuracy of 97.2%.

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Published

2023-10-15

How to Cite

Turnip, M., Priambodo, G. R., Sihaloho, T. D. ., Ndruru, J. H. P. ., Sigalingging, J., Salsabillah, & Panjaitan, H. D. (2023). Application of the ANFIS Method to Predict Satisfaction with Facilities and Infrastructure. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 3(1), 180–185. https://doi.org/10.59934/jaiea.v3i1.283