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All Journal Indonesian Journal of Data and Science
Nadindra Dwi Ariyanta
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Sugeno Fuzzy Personality Prediction System: An Approach to Overcoming Psychological Measurement Uncertainty Nadindra Dwi Ariyanta; Anik Nur Handayani
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.192

Abstract

Personality prediction is a significant field in psychological measurement, yet it faces challenges due to psychological data's ambiguous and uncertain nature. This study aims to develop a Sugeno-based fuzzy logic system for predicting personality types according to the Myers-Briggs Type Indicator (MBTI). The dataset includes synthetic personality data, incorporating age, introversion, sensing, thinking, and judging. The fuzzification process converts crisp input values into fuzzy variables, which are then processed using predefined fuzzy rules to generate personality predictions. The defuzzification step yields crisp outputs corresponding to MBTI types, demonstrating the system's ability to handle uncertainty and ambiguity effectively. Implementation and evaluation were conducted using Python and LabVIEW, revealing a satisfactory performance with a low error rate of 0.445. This study highlights the potential of fuzzy logic, particularly the Sugeno method, in enhancing accuracy and adaptability in personality prediction, contributing to applications in education, human resource management, and personalized digital services.
Co-Authors Anik Nur Handayani