Intelligent Fuzzy Distributed Computing Approach to Analyse the Social Media's Impact on Graduate Student's Mental Health

Resumen

This study explores the application of fuzzy numbers in ranking Likert-scale responses,
focusing on improving the accuracy and reliability of rankings in different
research areas. By utilizing Cronbach’s alpha, we assess the internal consistency and
reliability of the Likert-scale data collected from graduate students. The research applies
a novel fuzzy ranking method which is multi-criteria decision-making (MCDM) to
transform subjective ordinal data into a more robust, measurable format, addressing
the limitations of traditional ranking methods. Through this approach, the paper aims
to provide a more nuanced and reliable ranking system for surveys and questionnaires,
ultimately enhancing the interpretation of Likert-scale data in various fields such as
marketing, psychology, and education. The results highlighted the potential of fuzzy
logic in refining data analysis and the importance of reliability measures and Cronbach’s
alpha ensures data consistency. Also, resultant clustering effectively segments
students based on patterns in their mental and physical health indicators.