Institutional Repository | Satya Wacana Christian University: Recommender System for Mobile Phone Selection applying Extended Weighted Tree Similarity Algorithm T1 672007238 BAB I
Chapter 1
Introduction
1.1 Background
Nowadays, information technology has been rapidly
developing. Information technology has been applied in almost
every part of human life. There are many kinds of information
technology and mobile phone technology is one of its parts that
have most rapidly developing. Mobile phones have transformed
the way we communicate with friends and family, coordinate our
daily activities, and organize our lives (Dawe, 2007). Figure 1.1
show 4 mobile phones from 2 different times, 2001 and 2011.
(a)
(b)
Figure 1.1 (a) mobile phones on 2001 (b) mobile phones on 2011,
Both of them from same vendor
Data from Badan Pusat Statistik (BPS) show that phone’s
user has increased more than ten times and it is proportional with
the data of pulse production. From both data, it can be concluded
that mobile phone has been rapidly developing. Figure 1.2 shows
the data about phone’s user and figure 1.3 show the data about
pulse production.
This condition makes the market of mobile phone has
bigger demands every year, a condition that has positive and
negative effects. The positive effect is now people have many
options to choose a mobile phone because many companies, old
and new comer, compete to produce best mobile phone. And the
negative effect from it is the condition sometime makes people
feel difficult to decide. So many type of mobile phone from many
1
companies with their own features can make people feel difficult
to choose one that they need or they want to.
Figure 1.2 Data of Phone’s user
2
Figure 1.3 Data of Pulse Production
And now, what people need is a recommender system that
can help them to choose one, or maybe more, mobile phone that
they need or they want. In this research, the recommender system
will applies an algorithm that name is Extended Weighted Tree
Similarity Algorithm. This algorithm considers not just the
specification of mobile phone but also weighted of the
specification. One of reason why people feel difficult to choose a
mobile phone is because mobile phone has so many features now.
With this algorithm, people can decide how important of every
feature, and also other sides of mobile phone, like the price and
vendor, so they can get the mobile phone’s recommendation base
on the weighted of the specification as they want.
The inputs of this recommendation system are the
criteria of the mobile phone, including the mobile phone’s
feature, vendor and price, that the people want and the weighted
of the criteria. And the output is the similarity value between the
input and the mobile phones. The range of the similarity level is
from 0 to 1. The mobile phone that has closest value of the
3
similarity to 1 is the most similar with the criteria that the people
have been inputted. It also means that the mobile phone is the
most recommended.
This recommendation system must be applied in one
or more sites that have enough information about many types and
features of mobile phones from many vendors. And the site that is
used in this research is the site of Tabloid Pulsa.
1.2 Research Problem
This research have some problems to discuss, they are:
1. How to apply Extended Weighted Tree Similarity in a
recommender system to count the similarity between user’s
input and mobile phone’s data from Tabloid Pulsa’ site.
2. How to test and measure that the recommender system has
good quality as software.
1.3 Objective and Benefit
The objectives and benefits of this research are:
1. Designing and Building a recommender system that can give
right recommendation for mobile phone selection.
2. Applying Extended Weighted Tree Similarity Algorithm on a
recommender system.
1.4 Problem Scope
The scopes of problem of this research are:
1. The mobile phone data’s is used just from 10 vendors: PC
Tablet, Nokia, Blackberry, Apple, Samsung, Sony Ericsson,
Motorola, Nexian, HTC, and LG.
2. The criteria of the mobile phone that will be counted by
Weighted Tree Similarity Algorithm are: price, vendor and
feature (just main feature, not added feature).
3. The features of mobile phone that will be allowed to count
are: Dimension, Sound, Camera, Data, Battery, General
(Network), Screen, Memory, and feature.
4
4. The data of the mobile phones that is used in this research just
from the crawling result from Tabloid Pulsa’s site.
5. The mobile phones that will be recommended in this system
just best ten, sorted by their similarity value.
6. The application and questioner will be in Bahasa Indonesia
because this research was held in Indonesia.
1.5 Organization Study
To make this research’s report easier to read, this
research’s report is divided to five chapters. And the previews
of each chapter are:
Chapter 1: Introduction
This chapter discuss about background, research problem,
objective and benefit, and problem scope of this research
Chapter 2: Literature Review
This chapter discuss about previous researches and
theories of Mobile Phone, Weighted Tree Similarity
Algorithm, and Recommender System.
Chapter 3: Methodology
This chapter discuss about the research methodology that
is used in this research and the design of recommender system
application.
Chapter 4: Result and Analysis
This chapter contains the result and analysis of this
research.
Chapter 5: Conclusion and Suggestion
This chapter contains the conclusions and suggestions
of this research.
5
Introduction
1.1 Background
Nowadays, information technology has been rapidly
developing. Information technology has been applied in almost
every part of human life. There are many kinds of information
technology and mobile phone technology is one of its parts that
have most rapidly developing. Mobile phones have transformed
the way we communicate with friends and family, coordinate our
daily activities, and organize our lives (Dawe, 2007). Figure 1.1
show 4 mobile phones from 2 different times, 2001 and 2011.
(a)
(b)
Figure 1.1 (a) mobile phones on 2001 (b) mobile phones on 2011,
Both of them from same vendor
Data from Badan Pusat Statistik (BPS) show that phone’s
user has increased more than ten times and it is proportional with
the data of pulse production. From both data, it can be concluded
that mobile phone has been rapidly developing. Figure 1.2 shows
the data about phone’s user and figure 1.3 show the data about
pulse production.
This condition makes the market of mobile phone has
bigger demands every year, a condition that has positive and
negative effects. The positive effect is now people have many
options to choose a mobile phone because many companies, old
and new comer, compete to produce best mobile phone. And the
negative effect from it is the condition sometime makes people
feel difficult to decide. So many type of mobile phone from many
1
companies with their own features can make people feel difficult
to choose one that they need or they want to.
Figure 1.2 Data of Phone’s user
2
Figure 1.3 Data of Pulse Production
And now, what people need is a recommender system that
can help them to choose one, or maybe more, mobile phone that
they need or they want. In this research, the recommender system
will applies an algorithm that name is Extended Weighted Tree
Similarity Algorithm. This algorithm considers not just the
specification of mobile phone but also weighted of the
specification. One of reason why people feel difficult to choose a
mobile phone is because mobile phone has so many features now.
With this algorithm, people can decide how important of every
feature, and also other sides of mobile phone, like the price and
vendor, so they can get the mobile phone’s recommendation base
on the weighted of the specification as they want.
The inputs of this recommendation system are the
criteria of the mobile phone, including the mobile phone’s
feature, vendor and price, that the people want and the weighted
of the criteria. And the output is the similarity value between the
input and the mobile phones. The range of the similarity level is
from 0 to 1. The mobile phone that has closest value of the
3
similarity to 1 is the most similar with the criteria that the people
have been inputted. It also means that the mobile phone is the
most recommended.
This recommendation system must be applied in one
or more sites that have enough information about many types and
features of mobile phones from many vendors. And the site that is
used in this research is the site of Tabloid Pulsa.
1.2 Research Problem
This research have some problems to discuss, they are:
1. How to apply Extended Weighted Tree Similarity in a
recommender system to count the similarity between user’s
input and mobile phone’s data from Tabloid Pulsa’ site.
2. How to test and measure that the recommender system has
good quality as software.
1.3 Objective and Benefit
The objectives and benefits of this research are:
1. Designing and Building a recommender system that can give
right recommendation for mobile phone selection.
2. Applying Extended Weighted Tree Similarity Algorithm on a
recommender system.
1.4 Problem Scope
The scopes of problem of this research are:
1. The mobile phone data’s is used just from 10 vendors: PC
Tablet, Nokia, Blackberry, Apple, Samsung, Sony Ericsson,
Motorola, Nexian, HTC, and LG.
2. The criteria of the mobile phone that will be counted by
Weighted Tree Similarity Algorithm are: price, vendor and
feature (just main feature, not added feature).
3. The features of mobile phone that will be allowed to count
are: Dimension, Sound, Camera, Data, Battery, General
(Network), Screen, Memory, and feature.
4
4. The data of the mobile phones that is used in this research just
from the crawling result from Tabloid Pulsa’s site.
5. The mobile phones that will be recommended in this system
just best ten, sorted by their similarity value.
6. The application and questioner will be in Bahasa Indonesia
because this research was held in Indonesia.
1.5 Organization Study
To make this research’s report easier to read, this
research’s report is divided to five chapters. And the previews
of each chapter are:
Chapter 1: Introduction
This chapter discuss about background, research problem,
objective and benefit, and problem scope of this research
Chapter 2: Literature Review
This chapter discuss about previous researches and
theories of Mobile Phone, Weighted Tree Similarity
Algorithm, and Recommender System.
Chapter 3: Methodology
This chapter discuss about the research methodology that
is used in this research and the design of recommender system
application.
Chapter 4: Result and Analysis
This chapter contains the result and analysis of this
research.
Chapter 5: Conclusion and Suggestion
This chapter contains the conclusions and suggestions
of this research.
5