Design of a Lecturer Performance Evaluation Based on a Fuzzy Inference System with the Mamdani Method in the Field of Education and Teaching

Umi Hanik; Toto Nusantara; I Made Sulandra

doi:10.31764/jtam.v10i4.39131

Design of a Lecturer Performance Evaluation Based on a Fuzzy Inference System with the Mamdani Method in the Field of Education and Teaching

Umi Hanik, Toto Nusantara, I Made Sulandra

Abstract

Lecturer performance evaluation is an essential component in ensuring the quality of higher education. However, the complexity of the assessed aspects and the diversity of data types often make objective decision-making challenging. This study aims to develop a fuzzy logic-based lecturer performance evaluation system using the Mamdani method, which enables the simultaneous processing of qualitative and quantitative data. The model adopts a Multi Input Single Output (MISO) framework, with input variables representing several aspects of lecturer performance, including teaching, thesis supervision, and academic advising sourced from the Electronic Employee Performance Targets (e-EPT) of the Faculty of Teacher Training and Education, University of Trunodjoyo Madura. Data were collected through document analysis using a documentation guideline as the research instrument. Each variable is defined using triangular and trapezoidal membership functions. Through the fuzzification process, input data are transformed into degrees of membership, then processed using fuzzy inference rules, and finally converted into crisp values using centroid defuzzification, namely by determining the center of mass of the area under the curve. The centroid values are used to classify lecturer performance into four categories: Poor, Fair, Good, and Excellent. The proposed model was subsequently applied to the performance data of a lecturer as a case study and compared with the conventional method. The results showed that the conventional method produced an overall performance rating of Good, whereas the proposed the Fuzzy Inference System (FIS) with Mamdani method generated a centroid value of 81.6, corresponding to the Excellent category. In addition to producing the final performance category, the proposed FIS with Mamdani method provides information on the membership degrees of each assessment variable, rule activation strengths, and the contribution of each indicator to the final decision. These findings demonstrate that the proposed FIS with Mamdani method provides a more transparent, gradual, and comprehensive evaluation process than the conventional assessment method.

Keywords

Centroid Defuzzification; Decision Support System; Fuzzy Logic; Lecturer Performance; Mamdani Method.

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DOI: https://doi.org/10.31764/jtam.v10i4.39131