Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 91
FUZZY LOGIC APPROACH TO QUANTIFY PREFERENCE TYPE BASED ON MYERS BRIGGS TYPE INDICATOR MBTI
Hindriyanto Dwi Purnomo, Srie Yulianto Joko Prasetyo
Satya Wacana Christian University Jl. Diponegoro 52-60, Salatiga, Indonesia
Phone : 0298 326362 Email : mcIndrytelkom.net
ABSTRACT
Knowing our self is the first way to accept our condition. There are several option to know our self.
One of them is knowing our preference. When we know our preference, we can know our strength and
weakness. People who know their strength and weakness can develop their self optimally and they
can enjoy their life. There are a lot of method in measuring preference type. One of them are Myers
Briggs Type Indicator. Myers Briggs Type Indicator measure preference type based on four indicator :
nature extravert and introvert, the way in collect information sensing and intuition, the way in decide
thinking and feeling and life style percepting and judging. The indicator will create 16 combination of
preference type. This research goal is build a model to quantify preference type using fuzzy logic based on
Myers Briggs Type Indicator. The method try to quantity type preference. The computational model
use in this research is fuzzy logic. In the future, this method can develop to create personality on machine.
Keywords : Preference Type, MBTI, Fuzzy, Quantify
1. INTRODUCTION
Knowing our preference is early way to know our self. When we know our preference, we can do a job
or activity appropriate with our personality. This help us to do the job or activity comfortably, effectively,
creatively and happily.
Preference measurement is commonly used in psychology, mainly in human resource and
development. Myers Briggs Type Indicator MBTI is a preference model and inventory that widely used in
the world. According to Center for Application of Psychological Type, there are 2.000.000 people use
MBTI in a year. Preference type have a lot of advantage to develop personality. This is mostly use in
the field that need interact with a lot of people. This can be use in : determine a job, create an optimal
condition in a job, compose a learning method, build a solid team work, etc.
This research goal is build a model to quantify preference type using fuzzy logic based on Myers
Briggs Type Indicator. MBTI used because of its validity and reliability. In the future, the quantity
method can develop to create personality machine.
2. LITERATURE 2.1. Myers Briggs Type Indicator
Myers Briggs Type Indicator MBTI is an instrument for measuring person’s preference.
Preference is something that we choose to do ,if we have a choice, althought we have some alternative in
doing so. When we do in the way that we choose, we will use less energy and mind. The most frequency
example in determine preference is the way we make a sign. We will tend to use the familiar hand to make a
sign althought we can use the other hand. When we use the familiar hand, we will need less energy and
effort. This is feel comfort to us.
MBTI was first developed by Isabel Briggs Myers 1897-1979 and her mother, Katherine Cook Briggs
1875-1968. MBTI developed in 1943 based upon Carl Jung’s Swiss psychiatrist and colleague of
Freuds notion of psychological types. MBTI measuring a person’s preference using four indicator :
nature, the way in collect information, the way in decide and life style. Each indicator was scaled with
opposite poles : nature extravert and introvert, the way in collect information sensing and intuition, the
way in decide thinking and feeling and life style perceiving and judging. The indicator will create 16
combination of preference type. Each type was represented in four alphabed in example ENTP
extravert, intuition, thinking, perceiving.
According to Jung, there are no one that pure in one indicator, in example pure extravert or pure
intavert. Everyone have a combination from each indicator. In every indicator, one pole will dominate
the other. This dominant will more influence people preference.
Based on the nature, there are 2 opposite poles, extravert and introvert. This nature represent how
people interact with outer world activities, people, excitements and things and inner world thought,
interest,ideas and imagination. Extravert people will more influenced by outer world. Introvert people will
more influence by inner world.
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 92
Based on the way people take in information, there are 2 opposite poles, sensing and intuition. People
with a preference for sensing will comfort to start on the factual information before moving to the concept.
People with a preference for intuition will comfort to start on the idea or guess before moving to the factual.
Based on the way in decide, the opposite poles are thinking and feeling. A person with preference for
thinking use logical approachment to explain, cause- effect and universal rules of truth. They usually
common with harmony. A person with preference for feeling tend to analysis the impact of decition on the
other people.
Based on the life style, the opposite poles are judging and perceiving. People with preference for
judging tend to do something structured. People with preference for perceivig tend to enjoy the spontaneous
life.
2.2. Fuzzy Logic
Fuzzy logic is part of artificial intelligent that adopt the pattern of thinking method. This logic
support the problem that can’t be determine as true or false, black or white. This concept are no longer
clear-cut like yes or no, but relatively vague, for example more or less true.
Fuzzy logic arranged from fuzzy set who first developed by Lotfi Zadeh 1965. A fuzzy set allow
the degree of membership of an item in a set to be any real number between 0 and 1. This allow human
observation, expressions, and expertise to be more closely modeled. Fuzzy set are defined over some
universe of discourse depending on the problem Yan, 1994.
Fuzzy logic can effectively used in non linier and complex system usually difficult to make in
mathematic modeled which have uncertainty factor. There are two important thing in using fuzzy logic,
chosing the main parameter used in fuzzy logic and compose the knowledge base knowledge acquisition.
A fuzzy set is a generalisation of the concept of an ordinary set and defined by membership function. For
a universe of discourse U, the fuzzy set can denoted as :
} {
U u
u u
F
F
ε µ
| ,
=
for continous U, F may be written as : and for discrete U, as :
A fuzzy logic usually consists of : 1.
A fuzzyfication unit which maps measured inputs crisp into fuzzy linguistic.
2. A knowledge base KB which is the collection
of knowledge required to achive output. 3.
A fuzzy reasoning mechanism that perform various fuzzy logic operation to infer the output
for given fuzzy input. 4.
A defuzzificaton unit which convert the inferred fuzzy output into crisp values.
3. METHODOLOGY
There are several step in the preference method using in this research see fig-1.
Figure 1. Measurement Block
Input obtain from the MBTI. For each indicator, there are several questions that should be answer. The
question purpose are to guess audience preference type. The answer used to inventory the audience
preference, not state logical true or false. Each answer divide in 5 category that represend the intensity. The
weight of intensity will be used to quantify.
Each answer category used to build a membership function. The membership function used in this
research are Gaussian see fig-2.
Figure 2. membership function
Each indicator will supplay two fuzzy input from their opposite poles. This input will be compute using
fuzzy logic. The crisp output will represent the quantity of preference type.
The defuzzification unit strategies used in this research is centre of area COA method which
describe as :
Input using MBTI method
Quantify method using Fuzzy logic
Output
Fuzzy Logic Approach to Quantify Preference Type Based on Myers Briggs Type Indicator MBTI – Hindriyanto Dwi Purnomo Srie Yulianto Joko Prasetyo
ISSN 1858-1633 2005 ICTS 93
∫ ∫
= dz
z dz
z z
z µ
µ or
∑ ∑
= =
=
n j
j n
j j
j
z z
z z
1 1
µ µ
The fuzzy logic architecture used can be see in figure 3.Universal Plug Play UPnP was first
initiated by Microsoft [x], which initialized the work through the Plug and Play in Microsoft Windows 95
OS [x]. It is first implemented in Microsoft Windows ME OS. This concept of network architecture
framework was later developed within a forum called UPnP Forum. UPnP is meant to provide a transparent
automatic connectivity and interoperability between appliances, PCs, and services within a network, which
is based on peer-to-peer discovery and configuration. It is based on a number of standards protocols, such as
TCP, IP, UDP, HTTP, SOAP, SSDP, etc. This enhances the interoperability between UPnP devices.
Figure 1 shows the UPnP protocol stack.
Figure 3. Sketch of Fuzzy Logic
4. RESULT
