Determination of Thesis Preceptor and Examiner

Based on Specification of Teaching Using Fuzzy Logic

Andysah Putera Utama Siahaan Universitas Pembangunan Panca Budi

Jl. Dr. Mansur No. 9, Medan, Sumatra Utara, Indonesia andiesiahaan@gmail.com

Abstract— Determination of the preceptor is one of

academic obligations. Undesirable things always happen in

getting optimal decisions in which faculty are assigned not

the most appropriate to the topic of thesis. This matter can

affect the result and the quality of the thesis. The research

process uses the input variable of lecturers criteria. The

data will be processed by using the method of fuzzy logic

to obtain the output consists of preceptors and examiners.

In this case, the students do not have to worry about the

competence of the lectures since the lecturers who have

been given to them are fully filtered.

Keywords— Fuzzy, Teaching, Logic, Skill, Preceptor.

I. INTRODUCTION

Hearing preceptor in determining the development

application of thesis, fuzzy logic takes apart to determine and

prepare lecturers who are assigned to test the preceptor of

thesis. Preparat ion is carried out in the hope that the thesis

tested by lecturers who have expert ise in accordance with the

theme of those tested. Implementation of fuzzy logic here is

the calculation of the relationship between the lecturers skill.

We know that one lecturer can cover many subjects, but the

weight of all subjects are not similar. The weight of each

subject must be supported to calculate the result of the final

test. The lecturer who obtains the highest score will be the

preceptor of thesis.

II. THEORIES

Fuzzy logic is an approach to computing based on "degrees

of truth" rather than the usual "true or false" (1 or 0) Boolean

logic on which the modern computer is based. The idea of

fuzzy logic was first advanced by Dr. Lotfi Zadeh of the

University of California at Berkeley in the 1960s. Dr. Zadeh

was working on the problem of computer understanding of

natural language. Natural language (like most other activ ities

in life and indeed the universe) is not easily translated into the

absolute terms of 0 and 1. (Whether everything is ult imately

describable in b inary terms is a philosophical question worth

pursuing, but in practice much data we might want to feed a

computer is in some state in between and so, frequently, are

the results of computing.)

Classical logic only permits propositions having a value of

truth or falsity. The notion of whether 1+1=2 is an absolute,

immutable, mathematical truth. However, there exist certain

propositions with variable answers, such as asking various

people to identify a co lor. The notion of truth doesn't fall by

the wayside, but rather a means of representing and reasoning

over partial knowledge is afforded, by aggregating all possible

outcomes into a dimensional spectrum.

Both degrees of truth and probabilities range between 0 and

1 and hence may seem similar at first. For example, let a 100

ml glass contain 30 ml of water. Then we may consider two

concepts: empty and full. The meaning of each of them can be

represented by a certain fuzzy set. Then one might define the

glass as being 0.7 empty and 0.3 full. Note that the concept of

emptiness would be subjective and thus would depend on the

observer or designer. Another designer might, equally well,

design a set membership function where the glass would be

considered full for all values down to 50 ml. It is essential to

realize that fuzzy logic uses truth degrees as a mathematical

model of the vagueness phenomenon while probability is a

mathematical model of ignorance.

A. Applying Truth Values.

A basic application might characterize sub-ranges of a

continuous variable. For instance, a temperature measurement

for anti-lock brakes might have several separate membership

functions defining particular temperature ranges needed to

control the brakes properly. Each function maps the same

temperature value to a truth value in the 0 to 1 range. These

truth values can then be used to determine how the brakes

should be controlled.

Fig. 1 - Fuzzy Logic Example

In this image, the meanings of the expressions cold, warm,

and hot are represented by functions mapping a temperature

scale. A point on that scale has three "truth values"—one for

each of the three functions. The vertical line in the image

represents a particular temperature that the three arrows (truth

values) gauge. Since the red arrow points to zero, this

temperature may be interpreted as "not hot". The orange arrow

(pointing at 0.2) may describe it as "slightly warm" and the

blue arrow (pointing at 0.8) "fairly cold".

B. Linguistic Variables.

While variab les in mathematics usually take numerical

values, in fuzzy logic applicat ions, the non-numeric are often

used to facilitate the expression of ru les and facts. A linguistic

variable such as age may have a value such as young or its

antonym old. However, the great utility of linguistic variables

is that they can be modified via linguistic hedges applied to

primary terms. These linguistic hedges can be associated with

certain functions..

C. Early Applications.

The Japanese were the first to utilize fuzzy logic for

practical applicat ions. The first notable application was on the

high-speed train in Sendai, in which fuzzy logic was able to

improve the economy, comfort, and precision of the ride. It

has also been used in recognition of hand written symbols in

Sony pocket computers; flight aid for helicopters; controlling

of subway systems in order to improve driving comfort,

precision of halting, and power economy; improved fuel

consumption for automobiles; single-button control for

washing machines, automatic motor control for vacuum

cleaners with recognition of surface condition and degree of

soiling; and prediction systems for early recognition of

earthquakes through the Institute of Seis mology Bureau of

Metrology, Japan.

D. If-Then Rules.

Fuzzy set theory defines fuzzy operators on fuzzy sets. The

problem in apply ing this is that the appropriate fuzzy operator

may not be known. For example, a simple temperature

regulator that uses a fan might look like this:

IF temperature IS very cold THEN stop fan

IF temperature IS cold THEN turn down fan

IF temperature IS normal THEN maintain level

IF temperature IS hot THEN speed up fan

There is no "ELSE" – all of the rules are evaluated, because the

temperature might be "cold" and "normal" at the same time to

different degrees.

The AND, OR, and NOT operators of boolean logic exist in fuzzy

logic, usually defined as the minimum, maximum, and complement;

when they are defined this way, they are called the Zadeh operators.

So for the fuzzy variables x and y:

NOT x = (1 - truth(x))

x AND y = minimum(truth(x), truth(y))

x OR y = maximum(truth(x), truth(y))

III. DESIGN AND IMPLEMENTATION

In this section we discuss about the design of variables,

fuzzy set and membership function.

A. Input.

To make the calcu lation of fuzzy we need variables which

are divided into five specifications. They are:

- Algorithm & Programming (AP)

- Computer Network (CN)

- Mobile Programming (MP)

- Artificial Intelligence (AI)

- Soft Computing (SC)

Every lecturer will be given the weight from 0 to 100, where 0

is the lowest value and 100 is the highest one.

Tab. 2 - The Lecturers Skill

No. Name AP CN MP AI SC

1 Andie Siahaan 90 90 90 90 90

2 Michael Bolton 90 80 80 70 70

3 Van Damme 80 90 70 60 50

4 Tom Cruise 90 60 70 80 70

5 Chun Li 60 60 60 50 40

B. Output.

Output takes the form of a list of preceptors and examiners

thesis are ready to assist students in completing their scientific

works. There are 4 lecturers with the highest weight in

accordance with the specification requirements designated

thesis. Below is the list of specific skill:

- Grade 1 : Preceptor 1

- Grade 2 : Preceptor 2

- Grade 3 : Examiner 1

- Grade 4 : Examiner 2

C. Fuzzy Process.

The data those have been entered will be processed to

produce the optimal output. At this stage, each weight

specification will be matched with the needs of the material in

accordance with the specifications of each thesis .

The weight of lecturer will be div ided into three fuzzy sets.

There are LOW, MID and HIGH. If the result is in HIGH

section, this is the high priority to the lecturer who can assist

the student to complete the thesis. Below the membership

function of lecturer weight.

Fig. 2 - Lecture Membership Function

After making the membership function, we have to

calculate the weight of each skill. Now we can compare what

we have done after calculating all weight.

Tab. 2 - The Weight of Algorithm & Programming

No. Name AP LOW MID HIGH

1 Andie Siahaan 90 0 0 1

2 Michael Bolton 90 0 0 1

3 Van Damme 80 0 0 1

4 Tom Cruise 90 0 0 1

5 Chun Li 60 0 0,667 0

Tab. 3 - The Weight of Computer Network

No. Name CN LOW MID HIGH

1 Andie Siahaan 90 0 0 1

2 Michael Bolton 80 0 0 1

3 Van Damme 90 0 0 1

4 Tom Cruise 60 0 0,667 0

5 Chun Li 60 0 0,667 0

Tab.4 - The Weight of Mobile Programming

No. Name MP LOW MID HIGH

1 Andie Siahaan 90 0 0 1

2 Michael Bolton 80 0 0 1

3 Van Damme 70 0 0,333 0,5

4 Tom Cruise 70 0 0,333 0,5

5 Chun Li 60 0 0,667 0

Tab.5 - The Weight of Artificial Intelligence

No. Name AI LOW MID HIGH

1 Andie Siahaan 90 0 0 1

2 Michael Bolton 70 0 0,333 0,5

3 Van Damme 60 0 0,667 0

4 Tom Cruise 80 0 0 1

5 Chun Li 50 0 1 0

Tab. 6 - The Weight of Soft Computing

No. Name SC LOW MID HIGH

1 Andie Siahaan 90 0 0 1

2 Michael Bolton 70 0 0,333 0,5

3 Van Damme 50 0 1 0

4 Tom Cruise 70 0 0,333 0,5

5 Chun Li 40 0 0,667 0

The skills required will be calcu lated, then the weight is

mapped in the form of classification, so that lecturers who

deserve to be thesis preceptor and examiner is found. The next

stage, each lecturer will be sorted in descending order by

weight which has been obtained, in which the lecturer who

has the highest weight is at the top of the list and vice versa.

After getting the thesis preceptor and examiner, each lecturer

who has been appointed will be marked and given a counter

that stores the total number of students who have mentored or

tested. On the terms that have been determined, that every

lecturer can only lead or test 6 students each semester, so that

if the number of counters lecturer has reached 6, the lecturer

can no longer be selected in the system, or in other words they

are at the lowest level of the list of lecturers. To do so, the

weight is filled with 0 so that when the sorting process, the

lecturer is located at the bottom of the list.

V. CONCLUSION

Applications built using the concept of fuzzy logic can

easily determine the competence of a lecturer. So the research

that is built will be perfect. The process of determining the

thesis preceptor and examiner in this application is similar to

the conventional process that occurs because the algorithm

used is derived from the idea that the natural human way of

thinking, but by using this application, the academic

authorities will get more consistent results and optimal

without having the burden of to analyze each of the weight

specifications.

.

REFERENCES

Arabacioglu, B. C. (2010). "Using fuzzy inference system for architectural space analysis". Applied Soft Computing 10 (3): 926–937.

doi:10.1016/j.asoc.2009.10.011. Biacino, L.; Gerla, G. (2002). "Fuzzy logic, continuity and effectiveness". Archive for Mathematical Logic 41 (7): 643–667. doi:10.1007/s001530100128. ISSN 0933-5846.

Cox, Earl (1994). The fuzzy systems handbook: a practitioner's guide to building, using, maintaining fuzzy systems. Boston: AP Professional. ISBN 0-12-194270-8. Gerla, Giangiacomo (2006). "Effectiveness and Multivalued Logics".

Journal of Symbolic Logic 71 (1): 137–162. doi:10.2178/jsl/1140641166. ISSN 0022-4812. Hájek, Petr (1998). Metamathematics of fuzzy logic. Dordrecht: Kluwer. ISBN 0-7923-5238-6.

Hájek, Petr (1995). "Fuzzy logic and arithmetical hierarchy". Fuzzy Sets and Systems 3 (8): 359–363. doi:10.1016/0165-0114(94)00299-M. ISSN 0165-0114.