Sugeno Fuzzy Inference Method and MatLab Application Program for Simulation of Student Performance Evaluation in the Elementary Mathematics Learning Process

Abstract

The purpose of this study is to make a simulation for the assessment of student performance in elementary mathematics learning process by using fuzzy logic to overcome the problem of the assessment process of student performance. Besides that there is no special system that can optimize in providing support for teachers in conducting evaluations that are still manual calculations. One way of determining the calculation of student evaluation results can be facilitated by using the help of Artificial Intelligence (AI) considerations as an optimization. In consideration of the performance evaluation (performance) of these students using fuzzy logic with the sugeno inference system method. This sugeno method is a fuzzy inference method for rules which is represented in the form of IF-THEN, where the system output is not a fuzzy set, but in the form of a linear equation. The criteria used in the assessment of student performance include very unsuccesusful, unsuccessful, average, successful, and very successful. In this simulation the results displayed with manual calculations and MatLab calculations as a comparison of the results of the manual calculation result value of 45 while in the MatLab calculation the result value is 48,5. So it can be concluded that the difference caused by the level of accuracy of the results of the inference rule in manual calculations is less effective and sometimes even a lot of inference rules must be adjusted.

Prediction of Sunspots using Fuzzy Logic: A Triangular Membership Function-based Fuzzy C-Means Approach

“…Therefore, in order to replicate this human operator's control behavior, we must model our systems in a way that make it capable of handling ambiguous and imprecise knowledge. This is exactly what fuzzy logic system do [1], it succeeds where system is overly challenging and complicated. Fuzzy logic has been effectively used in numerous applications from speech [2] and script [3] recognitions to control systems like speed control of Non-Silent Permanent Magnet Synchronous Motors [4].…”

“…A fuzzy framework is a pool of fuzzy expert intelligence and knowledge base that rather than the conventional and definite Boolean logic, can trigger data in vague terms. Expert knowledge is a combination of both fuzzy membership functions (MF) and fuzzy rule-base that contains all the fuzzy rules and are of form: If (conditions are true or fulfilled) Then (consequences are inferred) [1].…”

“…3) Knowledgebase: It contains IF-THEN statements and fuzzy rules that helps in decision making based on linguistic information. 4) Defuzzification: It is the final stage of fuzzy system where a fuzzy set obtained by inference engine is converted to crisp output [1].…”