This document describes a study that used fuzzy logic to analyze student satisfaction data from a university. It collected rating data from 180 students on 7 factors that influence satisfaction: education experience, teachers, services/facilities, staff, student life, reputation, and engagement. It defined triangular membership functions for the 7 input and 1 output parameters. It generated over 2,000 rules from the input combinations but selected 63 randomly. The fuzzy model found the overall student satisfaction to be 71.4%, which is above average. It concluded that fuzzy logic can accurately model satisfaction and the university's services do not currently need upgrading.

1. Student Satisfaction
using Fuzzy Logic
Result and Analysis
-AMIT SINGH DAHAL
-G5638545

2. Overview:
 Introduction
 Fuzzy
System
 Fuzzy
Model
 Input
and Output Parameters
 Rules
 Conclusion
and Analysis

3. Introduction:
Objective:
-To know the Quality and Consistency of the University
-Hopefully improve in future based on the student’s evaluation
-student’s satisfaction on 7 Items:
- Education Experience
-Teachers
-Services and Facilities
-Service Staff
-Student Life
-Reputation of Faculty & University
-Student Engagement

4. Introduction:
-ask questionnaires on these 7 Items
-Each Items comprises of some questions
-Each questions has a rating of 0-10
-Students gives rating based on their experience and thinking
-After getting the ratings in each Item, we use the aggregation technique
to get the crisp value for each Items and convert it out of 100
-
Now, after getting the rating in 100, then our next step is to use fuzzy
logic and implement, since the values differ from each students

5. Fuzzy System:

Applying membership
function in fuzzification

Create and use fuzzy rule
sets

Use one of the method(Ex:
Centroid Method) for
defuzification

6. Fuzzy Model:

Used the Fuzzy ToolBox
provided by Matlab
7.12.0.635

Mamdani Fuzzy Inference
System for fuzzification

Triangular membership for
Input and Output
parameters

Centroid Method used for
deffuzification

7. Input and Output Parameter:

8. Input Parameter:

9. Input Parameter:

10. Output Parameter:

11. Output Parameter:

12. Rules:

our input variable is 7
and output variable
one with three
linguistic values each

altogether 3^7 i.e.
2187 rules

filtered and randomly
selected 63 rules

13. Conclusion and Analysis:

14. Conclusion and Analysis:

Gathered ratings from around 180 students from 1st to 4th year ICT

The overall of satisfaction got from the ICT student is 71.4

The satisfaction is more than average

When simply calculating statistically, we get 72.76, hence we can
conclude that Fuzzy Logic can be implemented to such satisfactory
results

As the satisfaction is over average, the services and other facilities
do not need to be upgraded for some years

But we should note, the output results directly depends on the rules
hence, many analysis should be done while defining rules

15. THANK YOU…
ANY QUESTIONS???