Open AccessLevel of student satisfaction on lecturer performance with fuzzy inference system (FIS) tsukamoto method
Author(s) -
Tulus Pramita Sihaloho,
Mahyuddin K. M. Nasution,
Zakarias Situmorang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012130
Subject(s) - fuzzy logic , fuzzy inference system , defuzzification , quality (philosophy) , process (computing) , computer science , mathematics education , artificial intelligence , fuzzy inference , inference , psychology , fuzzy control system , machine learning , adaptive neuro fuzzy inference system , fuzzy set , fuzzy number , philosophy , epistemology , operating system
To improve the quality of students in higher education is inseparable from how the performance of lecturers in the teaching-learning process. The quality of lecturers greatly influences how the quality of graduates will be. Qualified lecturers can be seen from the lecturers’ performance in delivering learning material, assessment, discipline, behaviour and appearance. Lecturer performance evaluation can be assessed by students and assessors by study programs through questionnaires in which there are several aspects of assessment. The data processing of the questionnaire will use the Tsukamoto fuzzy method. The Mamdani method is often also known as the average method. In this method, there are 4 the stage for obtaining output, namely the formation of fuzzy sets, application function implications, rule composition and defuzzification. With the fuzzy mamdani method, information will be generated in the form of output of the success level of the teaching lecturer.