![Real Time Key Element Extraction for Design of
In Flight Meal Services Based on Passenger’s Personality Traits
Taufik Djatna
Post Graduate Program of Agro-industrial Technology,
Bogor Agriculture University
Dramaga Bogor, West Java, 16680 Indonesia
taufik.djatna@ipb.ac.id
Hety Handayani Hidayat
Post Graduate Program of Agro-industrial Technology,
Bogor Agriculture University
Dramaga Bogor, West Java, 16680 Indonesia
hetyhhidayat@apps.ipb.ac.id
Abstract— In flight meal services is one of the most important
points to judge an airline as favorite to the passengers. It is
crucial to include personal trait in designing these services. Thus,
this work concentrated on formulating model of in-flight meal
services. First, by using Key Element Extraction (KEE), we
identify passenger’s personality traits in real time from social
media. And then we design model formulation of in-flight meal
services, by using Quantification Theory Type 1 (QTT1). The
identification of passenger’s personality traits in computational
experiment are based on particular type such as neophobia,
variety seeking selective, and variety seeking. Variants
formulation for each personality traits were designed for
different categories such as menu variant, originality,
appearance, suitability, cordiality, punctually and responsibility.
To enabling implementation of this model, it is required to attach
it to the current booking and database costumer system that
running online.
Keywords— In flight meal services; personality traits; real time;
design
I. INTRODUCTION
As air transportation industries grow, core business of the
airline is bringing passengers from one place to their
destinations. Also, airline businesses are in charged to give a
good service during their journeys [1,2,3,4,5]. For fulfilling the
passenger’s requirement, airline also completing their services
with set aside food what is known as term in flight meal [6].
In flight meal services is one of the factors on passenger’s
list for choosing an airline which is covering food (i.e quality,
volume, menu variation, and appearance), pricing, cordiality of
crew (particularly flight stewardess), getting information,
ordering method, and punctually [7]. In flight meal services is
related to personality traits. This argue was reinforced by result
of research that categorized the factor that was influenced
tourist food consumption as cultural or religious influences,
socio-demographic factors, food-related personality traits,
exposure effect or past experience, and motivational factors.
Due to that problem, airline must know the passenger’s
personality traits [8].
In today’s competitive environment with rapid innovation
in meal in flight, there is an increased need for fulfilling
passenger’s requirement in-flight meal services airline
business. Airlines have a challenge to serve the right meals for
right passengers on the right flight. For increasing their
competitiveness, they should implemented requirement for
their own in-flight meal services systems.
Enterprise needs a data analysis in real time mode [9]
belonging to in-flight meal services. Real time requirement will
roam the data warehouse which have a big volume and
wreaked the data on business operation rapidly. Also, real time
will be optimize the decision making process by supply the
conceptual data [10,11,12].
To make a real time access, business process must be
integrated with social media. In this case, social media defined
as information media online (internet based) that enable the
user for participating, sharing, and making content virtual
easily. It has capability to recording social interaction in large
number and real timely. Information are being unlimited by
time and space. But by using social media, personality traits of
costumer may be revealed for their own convenience.
Therefore this work aims to identify passenger’s
personality traits, and to design architectural real time key
element extraction of in-flight meal services based on
passenger’s personality traits.
II. METHODOLOGY
In this case, the most important information is passenger’s
requirements. That one of the factors to influence passenger’s
requirement is their own personality traits. So, airline should
identify personality of their passengers well. Based on that
information, airline should design formulation to reveal and
deploy favorable services that is suitable with their personality
traits. The methodologies are in this paper presented on Figure
1.
Figure 1. Research Framework
ICITEE 2014 Yogyakarta, 7-8 October 2014 ISBN: 978-1-4799-5302-8
-114-](https://image.slidesharecdn.com/contentproceedingsoficitee2014-arealtimekey-150831061745-lva1-app6892/85/Real-Time-Key-Element-Extraction-for-Design-of-In-Flight-Meal-Services-Based-on-Passenger-s-Personality-Traits-14-320.jpg)
![A. Preprocessing
Preprocessing is a requirement of in preparing the raw
data. So it is help to comprehend the undestanding of
information in future steps. These stages are consisted of
exchanging data from unstructured real time data to a
structured data and reducing irrelevant terms [13].
B. Key Element Extraction (KEE)
KEE is an algorithm for finding key persons and key
terms of a discussion by scoring passengers and terms in the
context of their significance in discussions. Higher scored
passengers are key persons having innovative and creative
ideas or potential for producing them. Higher scored terms are
key terms indicating or leading to innovative and creative
ideas. KEE is based on the idea of mutually reinforcing
relationship between passengers and terms: significant
passengers are the passengers using many significant terms,
and conversely, significant terms are the terms used by many
significant passengers [14]. The following questions detailed
each step of KEE as follows:
( ) ( ). ( ). ( )
i j E
i j i j j
p t
s p s t w p t t
(1)
( ) ( ). ( ). ( )
i j E
j i i j j
p t
s t s p w p t t
(2)
Where:
ip : passenger ith
(i =1,2,...,n)
jt : term jth
(j=1,2,....,m)
( )is p : Score of passengers
( )js t : Score of term (j=1,2,....,m)
( )i jw p t : Total passengers score of term
( )jt : Weight of term (based on interest rate)
KEE algorithm be has as simultaneously finding key terms
and key persons in network-based discussions. It formed as a
network-based discussion is (1) held for enhancing innovation
and creativity toward product conceptualization, (2) based on
participants posting and replying messages (3) on online
message boards or chat rooms. Those discussions were made
several attempts with different focus groups. A discussion is
represented by a weighted directed bipartite graph G (V, E)
where V and E are sets of nodes and weighted edges,
respectively. It demande the operator of exclusive selection.
Let VP be a set of passengers of the discussion on online
forum, tweeter, or Facebook and VT be a set of terms used by
the passengers. Thus V = VP ∪ VT, VP ∩ VT = φ. Let denote
an edge between pi ∈ VP and tj ∈ VT and its weight by (pi, tj)
and w (pi, tj), respectively. W (pi, tj) = m, if the participant pi
used the term tj m times [14].
C. Quantification Theory Type 1
The QTT1 is a method of qualitative and categorical
multiple regression analysis allowed inclusion of independent
variables that are categorical [15]. The steps of quantification
theory are following:
1. Determine the in-flight meal services attribute (Xn)
(n=1,2,.....,7)
2. Define the categories i of in-flight meal services attribute
(Xni). For example, in this case for menu variant there are 3
categories i.e vegetarian, moslem, and kosher (X11, X12,
X13).
3. Classify sample based on their attribute categories.
4. Evaluating the passenger’s preferences about samples.
5. Formulating the in-flight meal services by using QTT1 on
R langunge [15]
III. IDENTIFICATION OF PASSENGER’S PERSONALITY TRAITS
A. Preprocessing
Initially we provided data from social media are with
loaded passengers posting, tagging or tweeting data about
meals or services on large number of data without being
limited by time. Besides that, nowadays social media became
more popular for people to soulful or give expression to public
about what their mind. So, without direct questioner,
passenger personality traits can be known from social media
based on their posting or comment. Data that derived from
social media are unstructured data in the text form and
multimedia content. But it isn’t organized well certain in a
database. In this case the observed are comments, tags, photos,
post on the forum of internationally recognized Airline (for
example GA Indonesia) members which is then captured by
the HTML5.
HTML5 is a language that used to create web pages and a
core technology of internet and basic language of designing. It
adds many new syntactical features and tags that indicate
website designs with special effects and awesome layouts
[16]. The data obtained from cloud computing are enrolled in
social media. Cloud provides a virtual computer that provides
the ability for the user to operate in accordance with the
requirement.
Unstructured data which obtained from social media will
be treated into structured data in the form of a column matrix.
In the search text in social media, the data is divided into data
that provide information such as user profiles and data provide
information such as the user transaction. Transaction that
occurs can be either an opinion, or tags (like or dislike) of the
user. Based on the data structure irregularity text, then the
preprocessing process requires some initial stage which
basically is to prepare so that the text can be changed to be
more structured. Steps are as follows:
1. Tokenizing
Phase cut input string by each constituent word. For example,
this opinion was obtained from the discussion forum (see on
Table I).
2. Filtering
Filtering is the stage of taking important words from the
token. The algorithm can stop list (remove unimportant words)
or word list (save the important word).
ISBN: 978-1-4799-5302-8 Yogyakarta, 7-8 October 2014 ICITEE 2014
-115-](https://image.slidesharecdn.com/contentproceedingsoficitee2014-arealtimekey-150831061745-lva1-app6892/85/Real-Time-Key-Element-Extraction-for-Design-of-In-Flight-Meal-Services-Based-on-Passenger-s-Personality-Traits-15-320.jpg)
![TABLE I. TOKENIZING
Opinion on Forum Token Result
GA
189
KNO
CGK
on
march
flight
operated
by
737
800
NG
with
registration
number
PK
GFR
this
aircraft
already
had
the
sky
interior
onboard
attendants
were
Nice
and
friendly
greeted
passenger
with
traditional
salam
garuda
after
take off
there
was
a
hot
meal
service
for
this
2
hours
journey
had
choices
beef
rendang
rice
or
fried
fish
I
tasty
after
FA
distributed
an
ice cream
for
dessert
PTV
AVOD
decent
movies
short film
music
games
will
fly
again
3. Stemming
This process is the stage looking for the root word of each
word filtering results. The process results in filtering and
stemming stages as follows:
TABEL. II RESULT OF FILTERING AND STEMMING
Filter Steamming Participant
Nice
traditional
tasty
choice
Funny
unique
impression
variant
P1
B. Identification passenger’s personality traits by using Key
Element Extraction
In this step, KEE algorithm was used to identify the
personality of passengers. Data that has been preprocessed,
therefore it will be analyze score of each passenger S (pi).
These values were then grouped following the rules:
1. If S (pi) > (Min S (pi) + 2 Range S (pi) /3) , participant’s
personality traits is variety seeking
2. If (Min S (pi) + 2 Range S (pi) /3) <= S (pi) <= (Min S
(pi) + Range S (pi) /3) , participant’s personality traits is
seeking variety selective
3. If S (pi) < (Min S (pi) + Range S (pi) /3), participant's
personality traits is neophobia
Personalities traits are psychological factors are important
in influencing consumption patterns. This factor leads to
passenger characteristics that influence consumption behavior.
There are 2 types of passengers that food neophobia
personality and variety seeking [8].
Food neophobia is a term used for people who are reluctant
to try new foods and digest where feelings are more
dominated for selecting food. Human beings are omnivores
will try a variety of food sources but at some point will be
careful to digest food is toxic and dangerous. Therefore,
neophobia food can be conceptualized relative would have a
preference for food that is more familiar than the new food.
Other personality types that affect the selection of food are
variety seeking passengers. Variety seeking is the term used
for the personality of the person who likes looking for
something that is diverse (diversity) and different as a good
choice in service and food. This type has the flexibility to
adopt the food they consume [8].
By understanding the personality types of passengers, the
airline could determine the pattern of food and service to be
provided. In this study, consumers were divided into three
types namely personality trait neophobia, variety-seeking
selective, variety seeking. Data has been taken from social
media, personality known as passenger behavior like or do not
like new thing that be unstructured data input. In this paper
used hypothetical data. Assuming that there were 100
participants who comment, posting, or retweet on forum of
Internationally recognized Airline (for example, GA
Indonesia) about in flight meal services. α is weighted of the
term which obtained by using expert system software. The
greater the value of α indicates that the word is more
important and relevant with the personality traits.
In Table III, it was found there were 17 passengers which
have personality traits neophobia, 60 passengers were variety
seeking selective and the other were variety seeking. In other
word, if we generate this data as dashboard of passenger’s
personality traits are represented on Figure 2. Based on that
dashboard, airline business can predict the in flight meal
services that will they serve.
ICITEE 2014 Yogyakarta, 7-8 October 2014 ISBN: 978-1-4799-5302-8
-116-](https://image.slidesharecdn.com/contentproceedingsoficitee2014-arealtimekey-150831061745-lva1-app6892/85/Real-Time-Key-Element-Extraction-for-Design-of-In-Flight-Meal-Services-Based-on-Passenger-s-Personality-Traits-16-320.jpg)
![TABLE VI DESIGN FORMULATION OF IN FLIGHT MEAL SERVICES EACH PERSONALITY TRAITS
Attribute of In-flight meal
services
Neophobia Variety Seeking Selective Variety Seeking
Categories PCC Categories PCC Categories PCC
x1 x1.1 -2,136 0,480 0,877 0,430 -3,162 0,706
x1.2 0,011 -0,501 -0,567
x1.3 0,796 -0,079 1,470
x2 x2.1 0,514 0,262 1,456 0,864 -0,606 0,409
x2.2 -0,343 -0,971 0,404
x3 x3.1 -0,746 0,461 0,754 0,625 -1,865 0,824
x3.2 0,853 -0,861 2,131
x4 x4.1 -0,391 0,366 0,867 0,860 -0,044 0,068
x4.2 0,783 -1,735 0,088
x5 x5.1 -0,142 0,120 -0,113 0,184 0,843 0,706
x5.2 0,213 0,170 -1,265
x6 x6.1 -1,001 0,431 0,399 0,700 3,051 0,731
x6.2 1,165 -1,239 1,614
x6.3 -0,478 0,675 -1,772
x7 x7.1 -1,126 0,564 -0,529 0,486 -8,136 0,635
x7.2 1,689 0,793 1,220
Constant 3,615 4,427 4,185
R -0,477 0,741 0,214
R2
0,473 0,907 0,719
TABLE VII. DESIGN OF IN FLIGHT MEAL SERVICES EACH PERSONALITY TRAITS
Design X1 X2 X3 X4 X5 X6 X7
Neophobia Kosher old country Standard different more information middle time slow respond
variety seeking selective Vegetarian old country Unique Suitable more information quarter time finish quick respond
variety seeking Kosher foreign country Standard different greeting quarter time early quick respond
V. CONCLUSION
Passenger’s personality traits have a powerful relationship
with in-flight meal services. Our finding revealed the
personality traits as neophobia, variety seeking selective, and
variety seeking. Designed formulation of in-flight meal
services are different both in menu variant, originality,
appearance, suitability, cordiality, punctually and
responsibility. Implementation of this systems support the
airline business could contribute for decision making to
formulate passenger’s requirement in flight meal services. Our
recommandation are integreted this system with current
booking and membership database systems.
REFERENCES
[1] Transportation departemen Indonesia, “Airline market in Indonesia was
increasing rapidly”, Media Trans: Jakarta, 2012.
[2] Archa R, and Subha MV, “A study on service quality and passenger
satisfaction on indian airlines”, International Journal of Multidisciplinary
Research, vol 2 issue 2. ISSN 2231 5780, 2012.
[3] Janger JW and Dion VZ, “ailine service quality in south africa and
malaysia –an internatioal customer expectation approach”, vol.5 no 11,
2013.
[4] Feng CM and Kung YJ, “analysis airline service improvement strategy
through importance and performance analysis”, journal of Eastern asia
sosiety for transportation studies, vol 6, pp 782-797, 2005.
[5] Upadhyaya M, “Customer satisfaction measurement in airline services:
an empirical study of need –gap analysis”, International Journal of
Management and Strategy, vol 3 issue .5, 2012.
[6] Jones P. “Flight-Catering”, Behr’sVerlag: hamburg, 2007, p.39-55
[7] Bahraeni K, et al, “Measuring service quality and a comparative analysis
in airline industry”, Management Science Letters, vol.3, pp 275–280,
2013.
[8] Mak AHN, et al, “Factor Influencing tourist food consumption”,
International Journal of hospital Management, vol 31, issue 3, 2012, pp
928-936
[9] Azvine B., Cui, Z., Nauck, D.D. & Majeed, B, “Real Time Business
Intelligence for the Adaptive Enterprise,” The 3rd IEEE International
Conference on Enterprise Computing, ECommerce, and E-Services, pp.
1-11, 2006
[10] Botan I., Cho, Y., Derakhshan, R., Dindar, N., Haas, L.M., Kim, K. &
Tatbul, N, “Federated Stream Processing Support for Real- Time
Business Intelligence Applications”, In BIRTE 2009, pp. 14-31, 2009.
[11] Sandu I, “Operational and Real-Time Business Intelligence”, Revista
Informatica Economică, no.3(47), pp. 33-36, 2009.
[12] Popeanga, J and Ion L, “Real Time Business Intelligence for Utilities
Industry”, Database systems journal, vol. 3, no.4, 2012.
[13] Han J, and Kamber M, “Data Mining Concept and Techniques 2nd
edition”, Elsevier: London, 2006.
[14] Noriko IY, Xavier L, and David EG., “Discourse analysis and creativity
support for concept product design”, 2009.
[15] Hui, Shurong, et al, “the application of quantification theory 1 in site
quality evaluation of japanese larch tree (larix kaemferi)”, international
Journal of information and systems sciences, volume 5, number 3-4 pp
528-531, 2009.
[16] Zhang Y, “Developing Effect of HTML5 Technology in web game,”
International Journal on Computational Sciences & Applications (IJCSA)
Vo2, No.6, December 2012.
ISBN: 978-1-4799-5302-8 Yogyakarta, 7-8 October 2014 ICITEE 2014
-119-](https://image.slidesharecdn.com/contentproceedingsoficitee2014-arealtimekey-150831061745-lva1-app6892/85/Real-Time-Key-Element-Extraction-for-Design-of-In-Flight-Meal-Services-Based-on-Passenger-s-Personality-Traits-19-320.jpg)
Real Time Key Element Extraction for Design of In Flight Meal Services Based on Passenger’s Personality Traits
- 1. 2014 6th International Conference on Information Technology and Electrical Engineering Department of Electrical Engineering and Information Technology Faculty of Engineering, Universitas Gadjah Mada Jalan Grafika 2 Yogyakarta 55281, Indonesia 2014 6th International Conference on Information Technology and Electrical Engineering ISBN: 978-1-4799-5302-8
- 2. Technically Co-Sponsored by Supported by ICITEE 2014 Partners and Sponsors Co-organized by Organized by
- 3. On behalf of the organizing committee, it is our pleasure to welcome you to Yogyakarta, Indonesia, for our annual conference. This is the 6th conference that is held by the Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada. This year, the conference is differently called as Joint conference 2014 as there will be 4 parallel conferences, including: 1. ICITEE (International Conference of Information Technology and Electrical Engineering) 2014, 2. CITEE (Conference of Information Technology and Electrical Engineering) 2014, 3. RC-CIE (Regional Conference on Computer and Information Engineering) 2014, and 4. CCIO (Conference on Chief Information Officer) 2014. The joint conference’s theme is “Leveraging Research and Technology through University-Industry-Government Collaboration” emphasizes on the enhancement of research in a wide spectrum, including information technology, communication and electrical engineering, as well as e-services, e-government and information system. The conference is expected to provide excellent opportunity to meet experts, exchange information, and strengthen the collaboration among researchers, engineers, and scholars from academia, government, and industry. In addition, the conference committee has invited five renowned keynote speakers, Prof. Marco Aiello from University of Groningen (RuG), Netherland, Prof. Einoshin Suzuki from Kyushu University, Prof. Yoshio Yamamoto from Tokai University, Prof. Jun Miura from Toyohashi University of Technology, and Prof. Kazuhiko Hamamoto from Tokai University, Japan. The conference committee also invited Tony Seno Hartono from National Technology Officer of Microsoft Indonesia and Dr. Ing. Hutomo Suryo Wasisto (Associate Team Leader in MEMS/NEMS and Sensor Group) Technische Universität Braunschweig, Germany as Invited speaker to present their current research activities. This conference is technically co-sponsored by IEEE Indonesia Section. Furthermore, it is supported by JICA, AUN/SEED-Net, Ministry of Communication and Information Technology of The Republic of Indonesia, and King Mongkut’s Institute of Technology Ladkrabang, Thailand. As a General Chair, I would like to take this opportunity to express my deep appreciation to the organizing committee members for their hard work and contribution throughout this conference. I would also like to thank authors, reviewers, all speakers, and session chairs for their support to Joint Conference 2014. In addition to the outstanding scientific program, we hope that you will find time to explore Yogyakarta and the surrounding areas. Yogyakarta is city with numerous cultural heritages, natural beauty, and the taste of traditional Javanese cuisines, coupled with the friendliness of its people. Lastly, I would like to welcome you to Joint Conference 2014 and wish you all an enjoyable stay in Yogyakarta. Sincerely, Hanung Adi Nugroho, Ph.D. General Chair of Joint Conference 2014 Welcome Message from the General Chair -i-
- 4. On behalf of the technical program committee (TPC), we warmly welcome you to the 6th International Conference on Information Technology and Electrical Engineering (ICITEE 2014) in the cultural city of Yogyakarta, Indonesia. The committee has organized exciting technical programs for ICITEE 2014 with conference theme of “Leveraging Research and Technology through University-Industry Collaboration.” As an annual International conference, ICITEE provides excellent platform to share innovative idea and experiences, exchange information, and explore collaboration among researchers, engineers, practitioners and scholars the field of information technology, communications, and electrical engineering. All 163 submitted papers from 18 countries throughout the world went through a rigorous review process and each paper was evaluated by at least three independent reviewers in accordance with standard blind review process. Based on the results of the rigorous review process, 78 papers have been selected, which constitute the acceptance rate of 47.9%. These papers have been grouped into 5, ranging from the fields of information technology, communications, power systems, electronics, and control systems. Besides those regular sessions, ICITEE 2014 also features world-class keynote/plenary speeches and distinghuish-invited speakers that reflect the current research and development trends in the aforementioned fields. We are deeply indebted to all of our TPC members as well as our reviewers, who volunteered a considerable amount of their time and expertise to ensure a fair, rigorous, and timely review process. Many thanks should be given to our keynote and invited speakers who will share their experience in this conference. Last but not least, our sincere gratitude should be given to all authors for submitting their work to ICITEE 2014, which has allowed us to assemble a high quality technical program. Welcome to Yogyakarta and hope you will enjoy a wonderful experience in this traditional city of Indonesia. With best regards, TPC Chair Welcome Message from the TPC Chair -ii-
- 5. Committees Advisory Board Committee Adhi Susanto (Universitas Gadjah Mada, Indonesia) Dadang Gunawan (Universitas Indonesia, Indonesia) Yanuarsyah Haroen (Institut Teknologi Bandung, Indonesia) Kuncoro Wastuwibowo (IEEE Indonesia Section) T. Haryono (Universitas Gadjah Mada, Indonesia) Chanboon Sathitwiriyawong (KMITL, Thailand) Hidekazu Murata (Kyoto University, Japan) Ruttikorn Varakulsiripunth (Thai-Nichi Institute of Technology, Thailand) Lukito Edi Nugroho (Universitas Gadjah Mada, Indonesia) Son Kuswadi (PENS, Indonesia) Organizing Committee Sarjiya (Universitas Gadjah Mada, Indonesia) Eka Firmansyah (Universitas Gadjah Mada, Indonesia) Hanung Adi Nugroho (Universitas Gadjah Mada, Indonesia) I Wayan Mustika (Universitas Gadjah Mada, Indonesia) Adha Imam Cahyadi (Universitas Gadjah Mada, Indonesia) Sigit Basuki Wibowo (Universitas Gadjah Mada, Indonesia) Kuntpong Woraratpanya (KMITL, Thailand) Prapto Nugroho (Universitas Gadjah Mada, Indonesia) Teguh Bharata Adji (Universitas Gadjah Mada, Indonesia) Sumet Prabhavat (KMITL, Thailand) Natapon Pantuwong (KMITL, Thailand) Noor Akhmad Setiawan (Universitas Gadjah Mada, Indonesia) Indriana Hidayah (Universitas Gadjah Mada, Indonesia) Kitsuchart Pasupa (KMITL, Thailand) Avrin Nur Widiastuti (Universitas Gadjah Mada, Indonesia) Teerapong Leelanupab (KMITL, Thailand) Iswandi (Universitas Gadjah Mada, Indonesia) Budi Setiyanto (Universitas Gadjah Mada, Indonesia) Bimo Sunarfri Hantono (Universitas Gadjah Mada, Indonesia) Yusuf Susilo Wijoyo (Universitas Gadjah Mada, Indonesia) Agus Bejo (Universitas Gadjah Mada, Indonesia) Husni Rois Ali (Universitas Gadjah Mada, Indonesia) Azkario Rizky Pratama (Universitas Gadjah Mada, Indonesia) Lilik Suyanti (Universitas Gadjah Mada, Indonesia) Nawang Siwi (Universitas Gadjah Mada, Indonesia) Technical Program Committee Addy Wahyudie (United Arab Emirates University, UEA) Adha Imam Cahyadi (Universitas Gadjah Mada, Indonesia) Adhi Susanto (Universitas Gadjah Mada, Indonesia) Alagan Anpalagan (Ryerson University, Canada) Amirthalingam Ramanan (University of Jaffna, Sri Lanka) Andy Warner (Google) Anto Satriyo Nugroho (BPPT, Indonesia) Anton Satria Prabuwono (Universiti Kebangsaan Malaysia, Malaysia) Ardyono Priyadi (Institute of Technology Sepuluh Nopember, Indonesia) Armein Z. R. Langi (Bandung Institute of Technology, Indonesia) Awinash Anand (Kyushu University, Japan) Azwirman Gusrialdi (University of Central Florida, USA) Boonprasert Suravkratanasakul (KMITL, Thailand) Chalermsub Sangkavichitr (KMUTT, Thailand) Chanboon Sathitwiriyawong (KMITL, Thailand) Chotipat Pornavalai (KMITL, Thailand) Cuk Supriyadi Ali Nandar (BPPT, Indonesia) Dhany Arifianto (Institute of Technology Sepuluh Nopember, Indonesia) Dhomas Hatta Fudholi (La Trobe University, Australia) Eiji Okamoto (Nagoya Institute of Technology, Japan) Ekachai Leelarasmee (Chulalongkorn University, Thailand) Esa Prakasa (LIPI, Indonesia) F Danang Wijaya (Universitas Gadjah Mada, Indonesia) Fahkriy Hario P (Universitas Brawijaya, Indonesia) Fikri Waskito (Nanyang Technological University, Singapore) Gamantyo Hendrantoro (Institute of Technology Sepuluh Nopember, Indonesia) Gunawan Wibisono (Indonesia University, Indonesia) Gusti Agung Ayu Putri (Udayana University, Indonesia) Harris Simaremare (Universite de Haute Alsace, France) Haiguang Wang (Institute for Infocomm Research, Singapore) Haruichi Kanaya (Kyushu University, Japan) Heroe Wijanto (Institut Teknologi Telkom, Indonesia) Hutomo Suryo Wasisto (Technische Universität Braunschweig, Germany) I Ketut Gede Dharma Putra (Udayana University, Indonesia) I Made Yulistya Negara (Institute of Technology Sepuluh Nopember, Indonesia) I Nyoman Satya Kumara (Udayana University, Indonesia) I Putu Agung Bayupati (Udayana University, Indonesia) Ida Ayu Dwi Giriantari (Udayana University, Indonesia) Igi Ardiyanto (Toyohashi University of Technology, Japan) Issarachai Ngamroo (KMITL, Thailand) Ivanna Timotius (Satya Wacana Christian University, Indonesia) -iii-
- 6. Iwan Setiawan (Satya Wacana Christian University, Indonesia) Jaziar Radianti (University of Agder, Norway) Joko Siswantoro (Universitas Surabaya, Indonesia) Kang-Hyun Jo (Ulsan University, Korea) Kazunori Hayashi (Kyoto University, Japan) Kazuto Yano (ATR, Japan) Khoirul Anwar (Japan Advanced Institute of Science and Technology, Japan) Kitsuchart Pasupa (KMITL, Thailand) Koji Yamamoto (Kyoto University, Japan) Kuntpong Woraratpanya (KMITL, Thailand) Lesnanto Multa Putranto (Hokkaido University, Japan) Maleerat Sodanil (KMUNB, Thailand) Mamiko Inamori (Tokai University, Japan) Manop Phankokkruad (KMITL, Thailand) Marco Aiello (University of Groningen, Netherland) Maulahikmah Galinium (Swiss German University, Indonesia) Mauridhi Hery Purnomo (Institute of Technology Sepuluh Nopember, Indonesia) Mustansar Ghazanfar (University of Southampton, UK) Natapon Pantuwong (KMITL, Thailand) Nawat Kamnoonwatana (Mahidol University, Thailand) Nidapan Sureerattanan (Thai-Nichi Institute of Technology, Thailand) Nihan Tran (University of Melbourne, Australia) Nol Premasathian (KMITL, Thailand) Noopadol Maneerat (KMITL, Thailand) Nopporn Chotikakamthorn (KMITL, Thailand) Olarn Wongwirat (KMITL, Thailand) Pattaracha Lalitrojwong (KMITL, Thailand) Pichai Aree (Thamassat University, Thailand) Poramote Wardkein (KMITL, Thailand) Pornvalai Chotipat (KMITL, Thailand) Ramesh Pokharel (Kyushu University, Japan) Rohana Sapawi (University Malaysia Sarawak, Malaysia) Rony Seto (Institute of Technology Sepuluh Nopember, Indonesia) Rukmi Sari Hartati (Udayana University, Indonesia) Ruttikorn Varakulsiripunth (Thai-Nichi Institute of Technology, Thailand) Sakchai Thipchaksurat (KMITL, Thailand) Saiyan Saiyod (Khon Kaen University, Thailand) Selo Sulistyo (Universitas Gadjah Mada, Indonesia) Singha Chaveesuk (KMITL, Thailand) Sisdarmanto Adinandra (Universitas Islam Indonesia, Indonesia) Somjet Suppharangsan (Burapha University, Thailand) Sompong Valuvanathorn (Ubon Ratchathani University, Thailand) Sooksan Panichpapiboon (KMITL, Thailand) Soradech Krootjohn (KMUNB, Thailand) Sorawat Chivapreecha (KMITL, Thailand) Sumet Prabhavat (KMITL, Thailand) Sunisa Rimcharoen (Bhurapa University, Thailand) Sunu Wibirama (Tokai University, Japan) Supakit Nootyaskool (KMITL, Thailand) Supawan Amanab (KMITL, Thailand) Supot Nitsuwat (KMUNTB, Thailand) Surachai Chaitusaney (Chulalongkorn University, Thailand) Surapan Airphaiboon (KMITL, Thailand) Surin Kittitornkun (KMITL, Thailand) Suvapadee Aramvith (Chulalongkorn University) Teerapong Leelanupab (KMITL, Thailand) Thanisa Numnonda (KMITL, Thailand) Thippaya Chintakovid (KMUNB, Thailand) Udayanto Dwi Atmojo (The University of Auckland, New Zealand) Uke Kurniawan Usman (ITTelkom, Indonesia) Umar Khayam (Bandung Institute of Technology, Indonesia, Indonesia) Virach Sornlertlamvanich (NECTEC, Thailand) Wanchalem Pora (Chulalongkorn University) Wawta Techataweewan (Srinakharinwirot University, Thailand) Wayan Gede Ariastina (Udayana University, Indonesia) Wimol San-Um (Thai-Nichi Institute of Technology, Thailand) Widyawan (Universitas Gadjah Mada, Indonesia) Yasushi Kato (Tsuruoka National College and Technology, Japan) Yi Ren (National Chiao Tung University, Taiwan) Yoshikazu Washizawa (The University of Electro-Communications, Japan) Yoshimitsu Kuroki (Kurume National College of Technology, Japan) -iv-
- 7. -v- Technical Sessions Session 1. Software Engineering, Services, and Information Technology TS 1 – 01 A Hybrid Technique for Enhancement of Periductal Fibrosis Ultrasound Images for Cholangiocacinoma Surveillance ....................................................................................................................................................................... 2 Pichet Wayalun (Khon Kaen University, Thailand); Saiyan Saiyod (Khon Kaen University, Thailand) TS 1 – 02 A Real Time Mission-Critical Business Intelligence for Development of Mixture Composition on Aromatherapy Product Based on Customer Personality Type ............................................................................................................ 7 Taufik Djatna (Bogor Agricultral UnIversity, Indonesia); Ida Bagus Dharma Yoga Santosa (Bogor Agriculture University, Indonesia) TS 1 – 03 An Infrastructure-less Occupant Context-Recognition in Energy Efficient Building ............................................ 13 Azkario Rizky (Universitas Gadjah Mada, Indonesia); Widy Widyawan (Gadjah Mada University, Indonesia); Guntur Putra (Universitas Gadjah Mada, Indonesia) TS 1 – 04 An Integrated Model of Customer Repurchase Intention in B2C E-commerce....................................................... 19 Saowakhon Homsud (King Mongkut’s Institute of Technology Ladkrabang, Thailand); Singha Chaveesuk (King Mongkut’s Institute of Technology Ladkrabang, Thailand) TS 1 – 05 An Intuitive User Interface for Motion Retrieval on a Mobile Multi-touch Device ................................................... 25 Natta Tammachat (King Mongkut's Institute of Technology Ladkrabang, Thailand); Natapon Pantuwong (King Mongkut's Institute of Technology Ladkrabang, Thailand) TS 1 – 06 Automated Document Classification for News Article in Bahasa Indonesia Based on Term Frequency INVERSE Document Frequency (TF-IDF) Approach ..................................................................................................................... 29 Ari Aulia Hakim (Swiss German University, Indonesia); Alva Erwin (Swiss German University, Indonesia); Kho Eng (Swiss German University, Indonesia); Maulahikmah Galinium (Swiss German University, Indonesia); Wahyu Muliady (Akon Teknologi, Indonesia) TS 1 – 07 Automatic Leaf Color Level Determination for Need Based Fertilizer Using Fuzzy Logic on Mobile Application......................................................................................................................................................................... 33 Kestrilia Prilianti (Universitas Ma Chung, Indonesia) TS 1 – 08 Automatic Multi-Document Summarization for Indonesian Documents Using Hybrid Abstractive-Extractive Summarization Technique ............................................................................................................................................... 39 Glorian Yapinus (Swiss German University, Indonesia); Alva Erwin (Swiss German University, Indonesia); Maulahikmah Galinium (Swiss German University, Indonesia); Wahyu Muliady (Akon Teknologi, Indonesia) TS 1 – 09 Autonomous Monitoring Framework with Fallen Person Pose Estimation and Vital Sign Detection................... 44 Igi Ardiyanto (Toyohashi University of Technology, Japan); Jun Miura (Toyohashi University of Technology, Japan) TS 1 – 10 Benchmarking of Feature Selection Techniques for Coronary Artery Disease Diagnosis.................................... 50 Noor Akhmad Setiawan (Universitas Gadjah Mada, Indonesia); Dwi Wahyu Prabowo (Universitas Gadjah Mada, Indonesia); Hanung Adi Nugroho (Universitas Gadjah Mada, Indonesia) TS 1 – 11 Boosting Performance of Face Detection by Using an Efficient Skin Segmentation Algorithm ........................... 55 Mohammad Reza Mahmoodi (Isfahan University of Technology, Iran); Sayed Masoud Sayedi (Isfahan University of Technoly, Iran) TS 1 – 12 C2C E-Commerce Trust Level Measurement and Analysis........................................................................................ 61 Sayid Ali Hadi (Swiss German University, Indonesia); James Purnama (University of Indonesia, Indonesia); Moh. A. Soetomo (Swiss German University, Indonesia); Maulahikmah Galinium (Swiss German University, Indonesia) TS 1 – 13 Calories Analysis of Food Intake Using Image Recognition ...................................................................................... 67 Natta Tammachat (King Mongkut's Institute of Technology Ladkrabang, Thailand); Natapon Pantuwong (King Mongkut's Institute of Technology Ladkrabang, Thailand) TS 1 – 14 Contrast Measurement for No-Reference Retinal Image Quality Assessment........................................................ 71 Hanung Adi Nugroho (Universitas Gadjah Mada, Indonesia); Titin Yulianti (Universitas Gadjah Mada, Indonesia); Noor Akhmad Setiawan (Universitas Gadjah Mada, Indonesia); Dhimas Arief D (Universitas Gadjah Mada, Indonesia) TS 1 – 15 Digital Image Hashing Using Local Histogram of Oriented Gradients ..................................................................... 75
- 8. -vi- Iwan Setyawan (Satya Wacana Christian University, Indonesia); Ivanna Timotius (Satya Wacana Christian University, Indonesia) TS 1 – 16 Emoticon-based Steganography for Securing Sensitive Data................................................................................... 79 Tohari Ahmad (Institut Teknologi Sepuluh Nopember (ITS), Indonesia); Gregory Sukanto (Institut Teknologi Sepuluh Nopember (ITS), Indonesia); Hudan Studiawan (Institut Teknologi Sepuluh Nopember, Indonesia); Waskitho Wibisono (Institut Teknologi Sepuluh Nopember, Indonesia); Royyana Ijtihadie (Institut Teknologi Sepuluh Nopember (ITS), Indonesia) TS 1 – 17 Evaluation of Edge Orientation Histograms in Smile Detection ................................................................................ 85 Ivanna Timotius (Satya Wacana Christian University,Indonesia); Iwan Setyawan (Satya Wacana Christian University, Indonesia) TS 1 – 18 ICUMSA Identification of Granulated Sugar Using Discrete Wavelet Transform and Colour Moments.............. 90 Alfiah Rizky Diana Putri (Universitas Gadjah Mada, Indonesia); Adhi Susanto (Universitas Gadjah Mada, Indonesia); Litasari Litasari (Universitas Gadjah Mada, Indonesia) TS 1 – 19 Identification of Malignant Masses on Digital Mammogram Images ........................................................................ 96 Hanung Adi Nugroho (Universitas Gadjah Mada, Indonesia); Faisal N (Gadjah Mada University, Indonesia); Indah Soesanti (Universitas Gadjah Mada, Indonesia); Lina Choridah (Universitas Gadjah Mada, Indonesia) TS 1 – 20 Measuring Domain Decomposition Effect in Estuary Model Parallelization Using High Performance Computer............................................................................................................................................................................ 102 Santosa Sandy Putra (UNESCO IHE - Institute for Water Education, The Netherlands) TS 1 – 21 Mobile Tourism Services Model: A Contextual Tourism Experience Using Mobile Services ............................... 108 Ridi Ferdiana (Universitas Gadjah Mada, Indonesia); Bimo Sunarfri Hantono (Universitas Gadjah Mada, Indonesia) TS 1 – 22 Real Time Key Element Extraction for Design of In-Flight Meal Services Based on Passenger's Personality Traits ................................................................................................................................................................................... 114 Taufik Djatna (Bogor Agricultral UnIversity, Indonesia); Hety Handayani Hidayat (IPB, Indonesia) TS 1 – 23 Real Time Static Hand Gesture Recognition System Prototype for Indonesian Sign Language.......................... 120 Rudy Hartanto (Universitas Gadjah Mada, Indonesia); Adhi Susanto (Universitas Gadjah Mada, Indonesia); Paulus Santosa (Gadjah Mada University, Indonesia) TS 1 – 24 Release of Masking and FAME Performance Evaluation to Improve Speech Intelligibility on Cochlear Implant................................................................................................................................................................................ 126 Sena Sukmananda Suprapto (Institut Teknologi Sepuluh Nopember, Indonesia); Dhany Arifianto (Institut Teknologi Sepuluh Nopember, Indonesia); Sekartedjo Sekartedjo (Institut Teknologi Sepuluh Nopember, Indonesia) TS 1 – 25 Statistical Analysis of Popular Open Source Software Projects and Their Communities ....................................132 Andi Wahju Rahardjo Emanuel (Universitas Kristen Maranatha, Indonesia) TS 1 – 26 Text-Background Decomposition for Thai Text Localization and Recognition in Natural Scenes.......................138 Ungsumalee Suttapakti (King Mongkut's Institute of Technology Ladkrabang, Thailand); Kuntpong Woraratpanya (King Mongkut's Institute of Technology Ladkrabang, Thailand); Kitsuchart Pasupa (King Mongkut's Institute of Technology Ladkrabang, Thailand); Pimlak Boonchukusol (King Mongkut's Institute of Technology Ladkrabang, Thailand); Taravichet Titijaroonroj (King Mongkut's Institute of Technology Ladkrabang, Thailand); Rattaphon Hokking (King Mongkut's Institute of Technology Ladkrabang, Thailand); Yoshimitsu Kuroki (Kurume National College of Technology, Japan); Yasushi Kato (Tsuruoka National College of Technology, Japan) TS 1 – 27 The Study of Utilization of SIP in Mobile Monitoring Abnormal Events Wireless Sensor Network...................... 144 Andreo Yudertha (Gadjah Mada University, Indonesia); Widy Widyawan (Gadjah Mada University, Indonesia); Sujoko Sumaryono (Gadjah Mada University, Indonesia) TS 1 - 28 TIS Dishub DIY: An Implementation of Traveler Information System in Special Region of Yogyakarta.............. 150 Daniel Febrian Sengkey (Gadjah Mada University, Indonesia); Sayuri Egaravanda (Universitas Gadjah Mada, Indonesia); Lukito Nugroho (Universitas Gadjah Mada, Indonesia) TS 1 – 29 Website Quality Assessment for Portal Hospital Indonesia Using Gap Analysis ................................................... 156 Muhammad Adipridhana (Swiss German University, Indonesia); Maulahikmah Galinium (Swiss German University, Indonesia); Heru Ipung (Swiss German University, Indonesia) Session 2. Wireless Communications, Networking and Vehicular Technology TS 2 – 01 3D Artificial Material Characterization Using Rectangular Waveguide..................................................................... 164 Danang Wibowo (ITB, Indonesia); Achmad Munir (Institut Teknologi Bandung, Indonesia) TS 2 – 02 Design on FPGA of the IEEE 802.11p Standard Baseband OFDM Section Model................................................168
- 9. -vii- Budi Setiyanto (Universitas Gadjah Mada, Indonesia); Rani Aji (Universitas Gadjah Mada, Indonesia); Afatika Adianti (Universitas Gadjah Mada, Indonesia); Addin Suwastono (Universitas Gadjah Mada, Indonesia) TS 2 – 03 Development of Embedded Gateway for Wireless Sensor Network and Internet Protocol Interoperability.......174 Sigit Basuki Wibowo (Gadjah Mada University, Ireland); Guntur Putra (Universitas Gadjah Mada, Indonesia); Bimo Sunarfri Hantono (Universitas Gadjah Mada, Indonesia) TS 2 – 04 Dynamic CFO Reduction in Various Mobilities Based on Extended Kalman Filter for Broadband Wireless Access Technology .......................................................................................................................................................... 178 Asri Diliyanzah (Telkom University, Indonesia); Rina Pudjiastuti (Telkom University, Indonesia); Budi Syihabuddin (Telkom University, Indonesia) TS 2 – 05 Experimental Study on Improved Parametric Stereo for Bit Rate Scalable Audio Coding...................................184 Ikhwana Elfitri (Andalas University, Indonesia); Rahmadi Kurnia (Andalas University, Indonesia); Defry Harneldi (Andalas University, Indonesia) TS 2 – 06 FDTD Method for Scattering Parameters Extraction of Rectangular Waveguide Loaded with Anisotropic Dielectric Material......................................................................................................................................................189 Achmad Munir (Institut Teknologi Bandung, Indonesia); Maulana Randa (Badan Penelitian Dan Pengembangan Kementerian Pertahanan RI, Indonesia) TS 2 – 07 FSS-based Planar Bandpass Filter Using Strip Slotted-Lines................................................................................194 Eric Simbolon (Bandung Institute of Technology, Indonesia); Achmad Munir (Institut Teknologi Bandung, Indonesia) TS 2 – 08 High Gain RF Amplifier for Very Low Frequency Receiver Application.................................................................199 Rahmat Putera (Institut Teknologi Bandung, Indonesia); Achmad Munir (Institut Teknologi Bandung, Indonesia) TS 2 – 09 Investigation on Objective Performance of Closed-loop Spatial Audio Coding ..................................................... 203 Ikhwana Elfitri (Andalas University, Indonesia); Rahmadi Kurnia (Andalas University, Indonesia); Fitrilina Fitrilina (Andalas University, Indonesia) TS 2 – 10 Performance of Anti-Jamming Techniques with Bit Interleaving in OFDM-Based Tactical Communications.... 209 Pradini Puspitaningayu (Universitas Negeri Surabaya, Indonesia); Gamantyo Hendrantoro (Sepuluh Nopember Institut of Technology, Indonesia) TS 2 – 11 Performance of Repeat-Accumulate Codes (RAC) for Decode-and-Forward Wireless Relay Channel .............. 214 Daryus Chandra (Universitas Gadjah Mada, Indonesia); Adhi Susanto (Universitas Gadjah Mada, Indonesia); Sri Suning Kusumawardani (Universitas Gadjah Mada, Indonesia) TS 2 – 12 Reorganizing Fingerprint Information Using Intersection Technique for RFID-based Indoor Localization System................................................................................................................................................................................ 220 I Wayan Mustika (Universitas Gadjah Mada, Indonesia); Sisongkham Phimmasean (NUOL, Laos) TS 2 – 13 RSSI Based Analysis of Bluetooth Implementation for Intra-Car Sensor Monitoring ............................................ 225 Eka Firmansyah (UGM, Indonesia); Lafiona Grezelda (Gadjah Mada University, Indonesia); Iswandi Iswandi (Gadjah Mada University, Indonesia) Session 3. Power Systems TS 3 – 01 A Probabilistic Approach to Analyze and Model the Simultaneity of Power Produced by Wind Turbines in a Wind Farm.......................................................................................................................................................................... 232 Kaveh Malekian (Chemnitz University of Technology, Germany); Anne Göhlich (Chemnitz University of Technology, Germany); Liana Pop (Chemnitz University of Technology, Germany); Wolfgang Schufft (University of Technology Chemnitz, Germany) TS 3 – 02 An Improved Maximum Efficiency Control for Dual-Motor Drive Systems .............................................................. 239 Luiz Rizki Ramelan (Universitas Gadjah Mada, Indonesia); Eka Firmansyah (UGM, Indonesia); Tian-Hua Liu (National Taiwan University of Science and Technology, Taiwan); Shao-Kai Tseng (National Taiwan University of Science and Technology, Taiwan); Jing-Wei Hsu (National Taiwan University of Science and Technology, Taiwan) TS 3 – 03 CCT Computation Method Based on Critical Trajectory Using Simultaneous Equations for Transient Stability Analysis......................................................................................................................................................................245 Ardyono Priyadi (ITS, Indonesia); Ony Qudsi (Politeknik Elektronika Negeri Surabaya, Indonesia); Mauridhi Purnomo (Institut of Technology Sepuluh Nopember, Indonesia) TS 3 – 04 Comparison of Economic Models for Two Differently Configured Uninterrupted Power Supply Systems From User Electricity Bill Perspective...................................................................................................................................... 251 Awais Yousaf (The University of Lahore, Pakistan); Onaiza Yousaf (The University of Lahore, Pakistan); Durdana Yousaf (Lahore Electric Supply Company, Pakistan)
- 10. -viii- TS 3 – 05 Development of a Power Flow Software for Distribution System Analysis Based on Rectangular Voltage Using Python Software Package................................................................................................................................................ 255 Lukmanul Hakim (Universitas Lampung, Indonesia); Muhamad Wahidi (Universitas Lampung, Indonesia); Trisno Handoko (Universitas Lampung, Indonesia); Herri Gusmedi (Universitas Lampung, Indonesia); Noer Soedjarwanto (Universitas Lampung, Indonesia); Federico Milano (University College Dublin, Ireland) TS 3 – 06 Efficiency Improvement of a Solar Power Plant Using Combine Cycle: An Experimental Study on a Miniaturized Solar Power Station .......................................................................................................................................................... 260 Bishwajit Banik Pathik (American International University-Bangladesh, Bangladesh); Nipu Datta (American International University-Bangladesh, Bangladesh); Muhammad Najebul Ahmed (American International University-Bangladesh, Bangladesh); Roksana Liya (American International University-Bangladesh, Bangladesh); Nazia Zaman (American International University-Bangladesh, Bangladesh) TS 3 – 07 Flower Pollination Algorithm for Optimal Control in Multi-Machine System with GUPFC..................................... 265 Mohammad Musofa Mulya, Pambudy (Gadjah Mada University, Indonesia) TS 3 – 08 Frequency Dependent Model of Underground Cables for Harmonic Calculations in Frequency Domain.......... 271 Kaveh Malekian (Chemnitz University of Technology, Germany); Uwe Schmidt (Dresden University of Technology, Germany); Abdullah Hoshmeh (Chemnitz University of Technology, Germany); Ali Shirvani (TU Chemnitz, Germany) TS 3 – 09 Fuzzy Logic Principles for Wind Speed Estimation in Wind Energy Conversion Systems.................................... 278 Agus Naba (University of Brawijaya, Indonesia) TS 3 – 10 Investigation and Modeling of Transient Voltage Stability Problems in Wind Farms with DFIG and Crowbar System........................................................................................................................................................................282 Kaveh Malekian (Chemnitz University of Technology, Germany); Uwe Schmidt (Dresden University of Technology, Germany); Ali Shirvani (TU Chemnitz, Germany); Wolfgang Schufft (University of Technology Chemnitz, Germany) TS 3 – 11 Magnetic Flux Distribution Due to the Effect of Stator-Rotor Configuration in the Axial Machine....................... 290 Danang Wijaya (UGM, Indonesia); Nobal Rahadyan (Universitas Gadjah Mada, Indonesia); Husni Ali (UGM, Indonesia) TS 3 – 12 Maximum Power Point Tracking Algorithm for Photovoltaic System Under Partial Shaded Condition by Means Updating β Firefly Technique .......................................................................................................................................... 296 Yanuar Safarudin (Institut Teknologi Sepuluh Nopember, Indonesia); Ardyono Priyadi (ITS, Indonesia); Mauridhi Purnomo (Institut of Technology Sepuluh Nopember, Indonesia); Margo Pujiantara (ITS, Indonesia) TS 3 – 13 Multi-Resolution Complex Image Method of Horizontal Multilayer Earth ................................................................ 301 Qi Yang (Wuhan University, P.R. China) TS 3 – 14 On the Potential and Progress of Renewable Electricity Generation in Bali.........................................................307 Satya Kumara (Udayana University, Bali, Indonesia); Wayan G. Ariastina (Udayana University, Indonesia); I Sukerayasa (Udayana University, Indonesia); Ida Giriantari (Udayana University, Bali, Indonesia) TS 3 – 15 Optimal Configuration of PV-Wind turbine-Grid-Battery in Low Potency Energy Resources ............................... 313 D Fittrin (Universitas Gadjah Mada, Indonesia); D Wijaya (Universitas Gadjah Mada, Indonesia); Sasongko Pramono Hadi (Gadjah Mada University, Indonesia) TS 3 – 16 Optimal Solution of Reliability Constrained Unit Commitment Using Hybrid Genetic Algorithm-Priority List Method................................................................................................................................................................................ 319 Sarjiya Sarjiya (Gadjah Mada University, Indonesia); Arief Budi Mulyawan (Gadjah Mada University, Indonesia); Andi Sudiarso (Gadjah Mada University, Indonesia) TS 3 – 17 Partial Discharge Analysis Using PCA and ANN for the Estimation of Size and Position of Metallic Particle Adhering to Spacer in Gas-Insulated System............................................................................................................... 325 Firmansyah Nur Budiman (Universitas Gadjah Mada, Indonesia); Yasin Khan (King Saud University, Saudi Arabia) TS 3 – 18 Quantum Neural Network for State of Charge Estimation......................................................................................331 Kevin Gausultan Hadith Mangunkusumo (Universitas Gadjah Mada, Indonesia); Danang Wijaya (UGM, Indonesia); Yung-Ruei Chang (Institute of Nuclear Energy Research, Atomic Energy Council, Taiwan); Yih-Der Lee (Institute of Nuclear Energy Research, Taiwan); Kuo Lung Lian (National Taiwan University of Science and Technology, Taiwan) TS 3 – 19 Reducing Induction Motor Starting Current Using Magnetic Energy Recovery Switch (MERS) .......................... 336 Danang Wijaya (UGM, Indonesia); Sholihatta Aziz (UGM, Indonesia); Hartanto Prabowo (UGM, Indonesia) TS 3 – 20 The Dynamic Performance of Grid-Connected Fixed-Speed Wind Turbine Generator.......................................... 342 Husni Rois Ali (UGM, Indonesia) TS 3 – 21 TVAC PSO for Modal Optimal Control POD and PSS Coordination in UPFC .......................................................... 347 Rian Fatah Mochamad (UGM, Indonesia); Sasongko Pramono Hadi (Gadjah Mada University, Indonesia); Mokhammad Setyonegoro (UGM, Indonesia)
- 11. -ix- Session 4. Electronics, Circuits, and Systems TS 4 – 01 A Face Detector Based on Color and Texture .............................................................................................................. 354 Mohammad Reza Mahmoodi (Isfahan University of Technology, Iran); Sayed Masoud Sayedi (Isfahan University of Technoly, Iran) TS 4 – 02 Analysis of Single Excitation Signal for High Speed ECVT Data Acquisition System............................................ 360 Arbai Yusuf (CTECH Labs Edwar Technology Co., Indonesia); Imamul Muttakin (CTECH Labs Edwar Technology Co., Indonesia); Wahyu Widada (CTECH Labs Edwar Technology Co., Indonesia); Warsito P. Taruno (CTECH Labs Edwar Technology Co., Indonesia) TS 4 – 03 Pulley's Clamping Force and Axial Position Measurements for Electro-mechanical Continuously Variable Transmission in Automotive Applications..................................................................................................................... 366 Bambang Supriyo (Universiti Teknologi Malaysia, Malaysia); Kamarul Tawi (Universiti Teknologi Malaysia, Malaysia); Mohd Che Kob (Universiti Teknologi Malaysia, Malaysia); Izhari Mazali (Universiti Teknologi Malaysia, Malaysia); Mohd Che Kob (Universiti Teknologi Malaysia, Malaysia) TS 4 – 04 Reconfigurable Hardware Implementation of Gigabit UDP/IP Stack Based on Spartan-6 FPGA ........................370 Mohammad Reza Mahmoodi (Isfahan University of Technology, Iran); Sayed Masoud Sayedi (Isfahan University of Technoly, Iran); Batul Mahmoodi (Telecommunication Company of Isfahan, Iran) TS 4 – 05 The Performance of Three-Phase Four-Wire Grid-Connected Inverter with Enhanced Power Quality ............... 376 Susatyo Handoko (Universitas Gadjah Mada, Indonesia); Sasongko Pramono Hadi (Gadjah Mada University, Indonesia); Suharyanto Suharyanto (Gadjah Mada University, Indonesia); Eka Firmansyah (UGM, Indonesia) TS 4 – 06 Underwater Sound Propagation Characteristics At Mini Underwater Test Tank with Varied Salinity and Temperature....................................................................................................................................................................... 381 Niken Yuwono (Institut Teknologi Sepuluh Nopember, Indonesia); Dhany Arifianto (Institut Teknologi Sepuluh Nopember, Indonesia); Endang Widjiati (Institut Teknologi Sepuluh Nopember, Indonesia); Wirawan Wirawan (Institut Teknologi Sepuluh Nopember, Indonesia) Session 5. Control Systems TS 5 – 01 A Neural Network Structure with Parameter Expansion for Adaptive Modeling of Dynamic Systems ................ 388 Erwin Sitompul (President University, Indonesia) TS 5 – 02 A New Approach in Self-Generation of Fuzzy Logic Controller by Means of Genetic Algorithm ........................394 Erwin Sitompul (President University, Indonesia); Iksan Bukhori (President University, Indonesia) TS 5 – 03 Double Target Potential Field.......................................................................................................................................... 400 Ferry Manalu (Universitas Katolik Indonesia Atma Jaya, Indonesia) TS 5 – 04 Robust Residual Generation for Sensor Fault Isolation in Systems with Structured Uncertainty........................ 405 Samiadji Herdjunanto (Gadjah Mada University, Indonesia); Adhi Susanto (Universitas Gadjah Mada, Indonesia); Oyas Wahyunggoro (UGM, Indonesia) TS 5 – 05 Design of Decoupled Repetitive Control for MIMO Systems ..................................................................................... 411 Edi Kurniawan (Indonesian Institute of Sciences, Indonesia); Riyo Wardoyo (Indonesian Institute of Sciences, Indonesia); Oka Mahendra (Indonesian Institute of Sciences, Indonesia)
- 13. Session 1 Software Engineering, Services and Information Technology
- 14. Real Time Key Element Extraction for Design of In Flight Meal Services Based on Passenger’s Personality Traits Taufik Djatna Post Graduate Program of Agro-industrial Technology, Bogor Agriculture University Dramaga Bogor, West Java, 16680 Indonesia taufik.djatna@ipb.ac.id Hety Handayani Hidayat Post Graduate Program of Agro-industrial Technology, Bogor Agriculture University Dramaga Bogor, West Java, 16680 Indonesia hetyhhidayat@apps.ipb.ac.id Abstract— In flight meal services is one of the most important points to judge an airline as favorite to the passengers. It is crucial to include personal trait in designing these services. Thus, this work concentrated on formulating model of in-flight meal services. First, by using Key Element Extraction (KEE), we identify passenger’s personality traits in real time from social media. And then we design model formulation of in-flight meal services, by using Quantification Theory Type 1 (QTT1). The identification of passenger’s personality traits in computational experiment are based on particular type such as neophobia, variety seeking selective, and variety seeking. Variants formulation for each personality traits were designed for different categories such as menu variant, originality, appearance, suitability, cordiality, punctually and responsibility. To enabling implementation of this model, it is required to attach it to the current booking and database costumer system that running online. Keywords— In flight meal services; personality traits; real time; design I. INTRODUCTION As air transportation industries grow, core business of the airline is bringing passengers from one place to their destinations. Also, airline businesses are in charged to give a good service during their journeys [1,2,3,4,5]. For fulfilling the passenger’s requirement, airline also completing their services with set aside food what is known as term in flight meal [6]. In flight meal services is one of the factors on passenger’s list for choosing an airline which is covering food (i.e quality, volume, menu variation, and appearance), pricing, cordiality of crew (particularly flight stewardess), getting information, ordering method, and punctually [7]. In flight meal services is related to personality traits. This argue was reinforced by result of research that categorized the factor that was influenced tourist food consumption as cultural or religious influences, socio-demographic factors, food-related personality traits, exposure effect or past experience, and motivational factors. Due to that problem, airline must know the passenger’s personality traits [8]. In today’s competitive environment with rapid innovation in meal in flight, there is an increased need for fulfilling passenger’s requirement in-flight meal services airline business. Airlines have a challenge to serve the right meals for right passengers on the right flight. For increasing their competitiveness, they should implemented requirement for their own in-flight meal services systems. Enterprise needs a data analysis in real time mode [9] belonging to in-flight meal services. Real time requirement will roam the data warehouse which have a big volume and wreaked the data on business operation rapidly. Also, real time will be optimize the decision making process by supply the conceptual data [10,11,12]. To make a real time access, business process must be integrated with social media. In this case, social media defined as information media online (internet based) that enable the user for participating, sharing, and making content virtual easily. It has capability to recording social interaction in large number and real timely. Information are being unlimited by time and space. But by using social media, personality traits of costumer may be revealed for their own convenience. Therefore this work aims to identify passenger’s personality traits, and to design architectural real time key element extraction of in-flight meal services based on passenger’s personality traits. II. METHODOLOGY In this case, the most important information is passenger’s requirements. That one of the factors to influence passenger’s requirement is their own personality traits. So, airline should identify personality of their passengers well. Based on that information, airline should design formulation to reveal and deploy favorable services that is suitable with their personality traits. The methodologies are in this paper presented on Figure 1. Figure 1. Research Framework ICITEE 2014 Yogyakarta, 7-8 October 2014 ISBN: 978-1-4799-5302-8 -114-
- 15. A. Preprocessing Preprocessing is a requirement of in preparing the raw data. So it is help to comprehend the undestanding of information in future steps. These stages are consisted of exchanging data from unstructured real time data to a structured data and reducing irrelevant terms [13]. B. Key Element Extraction (KEE) KEE is an algorithm for finding key persons and key terms of a discussion by scoring passengers and terms in the context of their significance in discussions. Higher scored passengers are key persons having innovative and creative ideas or potential for producing them. Higher scored terms are key terms indicating or leading to innovative and creative ideas. KEE is based on the idea of mutually reinforcing relationship between passengers and terms: significant passengers are the passengers using many significant terms, and conversely, significant terms are the terms used by many significant passengers [14]. The following questions detailed each step of KEE as follows: ( ) ( ). ( ). ( ) i j E i j i j j p t s p s t w p t t (1) ( ) ( ). ( ). ( ) i j E j i i j j p t s t s p w p t t (2) Where: ip : passenger ith (i =1,2,...,n) jt : term jth (j=1,2,....,m) ( )is p : Score of passengers ( )js t : Score of term (j=1,2,....,m) ( )i jw p t : Total passengers score of term ( )jt : Weight of term (based on interest rate) KEE algorithm be has as simultaneously finding key terms and key persons in network-based discussions. It formed as a network-based discussion is (1) held for enhancing innovation and creativity toward product conceptualization, (2) based on participants posting and replying messages (3) on online message boards or chat rooms. Those discussions were made several attempts with different focus groups. A discussion is represented by a weighted directed bipartite graph G (V, E) where V and E are sets of nodes and weighted edges, respectively. It demande the operator of exclusive selection. Let VP be a set of passengers of the discussion on online forum, tweeter, or Facebook and VT be a set of terms used by the passengers. Thus V = VP ∪ VT, VP ∩ VT = φ. Let denote an edge between pi ∈ VP and tj ∈ VT and its weight by (pi, tj) and w (pi, tj), respectively. W (pi, tj) = m, if the participant pi used the term tj m times [14]. C. Quantification Theory Type 1 The QTT1 is a method of qualitative and categorical multiple regression analysis allowed inclusion of independent variables that are categorical [15]. The steps of quantification theory are following: 1. Determine the in-flight meal services attribute (Xn) (n=1,2,.....,7) 2. Define the categories i of in-flight meal services attribute (Xni). For example, in this case for menu variant there are 3 categories i.e vegetarian, moslem, and kosher (X11, X12, X13). 3. Classify sample based on their attribute categories. 4. Evaluating the passenger’s preferences about samples. 5. Formulating the in-flight meal services by using QTT1 on R langunge [15] III. IDENTIFICATION OF PASSENGER’S PERSONALITY TRAITS A. Preprocessing Initially we provided data from social media are with loaded passengers posting, tagging or tweeting data about meals or services on large number of data without being limited by time. Besides that, nowadays social media became more popular for people to soulful or give expression to public about what their mind. So, without direct questioner, passenger personality traits can be known from social media based on their posting or comment. Data that derived from social media are unstructured data in the text form and multimedia content. But it isn’t organized well certain in a database. In this case the observed are comments, tags, photos, post on the forum of internationally recognized Airline (for example GA Indonesia) members which is then captured by the HTML5. HTML5 is a language that used to create web pages and a core technology of internet and basic language of designing. It adds many new syntactical features and tags that indicate website designs with special effects and awesome layouts [16]. The data obtained from cloud computing are enrolled in social media. Cloud provides a virtual computer that provides the ability for the user to operate in accordance with the requirement. Unstructured data which obtained from social media will be treated into structured data in the form of a column matrix. In the search text in social media, the data is divided into data that provide information such as user profiles and data provide information such as the user transaction. Transaction that occurs can be either an opinion, or tags (like or dislike) of the user. Based on the data structure irregularity text, then the preprocessing process requires some initial stage which basically is to prepare so that the text can be changed to be more structured. Steps are as follows: 1. Tokenizing Phase cut input string by each constituent word. For example, this opinion was obtained from the discussion forum (see on Table I). 2. Filtering Filtering is the stage of taking important words from the token. The algorithm can stop list (remove unimportant words) or word list (save the important word). ISBN: 978-1-4799-5302-8 Yogyakarta, 7-8 October 2014 ICITEE 2014 -115-
- 16. TABLE I. TOKENIZING Opinion on Forum Token Result GA 189 KNO CGK on march flight operated by 737 800 NG with registration number PK GFR this aircraft already had the sky interior onboard attendants were Nice and friendly greeted passenger with traditional salam garuda after take off there was a hot meal service for this 2 hours journey had choices beef rendang rice or fried fish I tasty after FA distributed an ice cream for dessert PTV AVOD decent movies short film music games will fly again 3. Stemming This process is the stage looking for the root word of each word filtering results. The process results in filtering and stemming stages as follows: TABEL. II RESULT OF FILTERING AND STEMMING Filter Steamming Participant Nice traditional tasty choice Funny unique impression variant P1 B. Identification passenger’s personality traits by using Key Element Extraction In this step, KEE algorithm was used to identify the personality of passengers. Data that has been preprocessed, therefore it will be analyze score of each passenger S (pi). These values were then grouped following the rules: 1. If S (pi) > (Min S (pi) + 2 Range S (pi) /3) , participant’s personality traits is variety seeking 2. If (Min S (pi) + 2 Range S (pi) /3) <= S (pi) <= (Min S (pi) + Range S (pi) /3) , participant’s personality traits is seeking variety selective 3. If S (pi) < (Min S (pi) + Range S (pi) /3), participant's personality traits is neophobia Personalities traits are psychological factors are important in influencing consumption patterns. This factor leads to passenger characteristics that influence consumption behavior. There are 2 types of passengers that food neophobia personality and variety seeking [8]. Food neophobia is a term used for people who are reluctant to try new foods and digest where feelings are more dominated for selecting food. Human beings are omnivores will try a variety of food sources but at some point will be careful to digest food is toxic and dangerous. Therefore, neophobia food can be conceptualized relative would have a preference for food that is more familiar than the new food. Other personality types that affect the selection of food are variety seeking passengers. Variety seeking is the term used for the personality of the person who likes looking for something that is diverse (diversity) and different as a good choice in service and food. This type has the flexibility to adopt the food they consume [8]. By understanding the personality types of passengers, the airline could determine the pattern of food and service to be provided. In this study, consumers were divided into three types namely personality trait neophobia, variety-seeking selective, variety seeking. Data has been taken from social media, personality known as passenger behavior like or do not like new thing that be unstructured data input. In this paper used hypothetical data. Assuming that there were 100 participants who comment, posting, or retweet on forum of Internationally recognized Airline (for example, GA Indonesia) about in flight meal services. α is weighted of the term which obtained by using expert system software. The greater the value of α indicates that the word is more important and relevant with the personality traits. In Table III, it was found there were 17 passengers which have personality traits neophobia, 60 passengers were variety seeking selective and the other were variety seeking. In other word, if we generate this data as dashboard of passenger’s personality traits are represented on Figure 2. Based on that dashboard, airline business can predict the in flight meal services that will they serve. ICITEE 2014 Yogyakarta, 7-8 October 2014 ISBN: 978-1-4799-5302-8 -116-
- 17. TABLE III. RESULT OF KEE ALGORITHM Α 0,15 0,1 0,1 0,1 0,1 0,03 0,05 0,15 0,2 0,02 1 Passengers (P)/Term (T) New Unique Variant Different Impression Cute Funny Challenge Foreign Oriental Total S (pi) Personality Traits 1 0 1 0 0 0 0 1 1 0 1 47,05 variety seeking selective 2 1 1 1 0 1 0 0 1 1 1 61,25 variety seeking selective 3 1 1 1 0 0 1 1 1 1 0 67,4 variety seeking 4 0 0 1 0 0 0 0 1 1 1 68,1 variety seeking 5 0 0 1 1 0 1 0 0 0 0 62,67 variety seeking selective 6 0 0 0 0 1 0 0 1 1 1 44,74 variety seeking selective 7 1 1 0 0 0 1 1 1 0 1 44,02 variety seeking selective 8 0 1 0 1 1 0 0 0 0 0 45,82 variety seeking selective 9 1 1 1 0 1 1 1 1 0 1 45,34 variety seeking selective 10 0 0 1 1 1 1 1 1 0 1 62,57 variety seeking selective 11 0 1 0 1 0 0 0 0 0 1 53,07 variety seeking selective 12 1 0 0 0 1 1 0 1 1 0 50,94 variety seeking selective 13 0 0 1 0 1 0 0 0 0 1 59,12 variety seeking selective 14 0 0 0 0 0 0 1 1 0 0 45,87 variety seeking selective 15 0 1 1 0 1 1 0 1 1 1 43,32 variety seeking selective 16 1 1 1 0 0 0 1 0 0 0 46,65 variety seeking selective 17 0 1 0 1 1 0 0 1 0 0 37,37 Neophobia 18 1 1 0 1 0 1 1 1 0 1 38,69 Neophobia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 1 0 1 1 0 0 1 1 0 1 19,32 Neophobia 91 1 1 1 1 0 0 0 0 0 0 34,52 Neophobia 92 0 0 0 1 1 0 0 1 1 0 34,62 Neophobia 93 1 0 1 0 1 0 0 1 0 1 60,39 variety seeking selective 94 0 1 0 0 0 1 0 1 1 1 69,29 variety seeking 95 1 0 0 0 1 1 0 0 1 1 54,22 variety seeking selective 96 0 0 0 1 1 0 1 0 1 1 41,37 variety seeking selective 97 1 1 1 1 1 0 0 0 1 0 38,72 Neophobia 98 1 0 1 0 0 1 0 0 0 0 51,22 variety seeking selective 99 1 1 1 0 1 0 0 0 1 1 26,87 Neophobia 100 0 1 0 0 0 1 0 1 0 0 42,8 variety seeking selective Total t (ti) 51 47 53 50 43 49 46 45 53 53 Figure 2. Dashboard Pessenger’s Personality Traits IV. DESIGN OF IN FLIGHT MEAL SERVICES BASED ON PASSENGER’S PERSONALITY TRAITS Based on the result of KEE algorithm, the passenger’s personality traits were being guidance to design in flight meal services. In this case, the formulation design obtained by using Quantification theory type 1 (QTT 1). Knowing attributes of in-flight meal services is an important step that should be done if we will formulate its design. In this paper, discussing 7 attribute on scope of our problem. Not only about meals but also about how these meals are served. The attributes about meals are including: a. Menu variant (X1) Menu variant (X1) has 3 categories which usual be on the market that is vegetarian (X11) that meals haven’t an animal protein; Moslem (X12) that meals haven’t alcohols and insurable as halal food; and Kosher (X13) that food for person that have interdiction. b. Originality (X2) In this case, originality that mean place where are the meals come from. Based on point of departure, Originality only derived as 2 categories namely foreign country (X21) and old country (X22). c. Appearances (X3) Presentation of our meals with appetite for its consumption. So, airline business must pay attention about appearance. This attribute have 2 categories namely unique (X31) such as using banana’s leaf or another materials that refer to local wisdom; and standard (X32) such as using the dishes standard. The attributes about service are including: a. Suitability (X4) Suitability decipherable as level of conformity between the images of menu and meals that be served. if they are similar then belonging to suitable category (X41), and inversely as different category (X42). ISBN: 978-1-4799-5302-8 Yogyakarta, 7-8 October 2014 ICITEE 2014 -117-
- 18. b. Cordiality (X5) In this paper, cordiality is about attitude of flight stewardess when they offer the menu. They only greeting (X51) or they give more information (X51). c. Punctually (X6) Meals must be served on right time. Punctually has 3 categorizes that are quarter time earlier (X61), middle time (X62), or quarter time finish (X63). d. Responsibility (X7) Responsibility derived as slow respond (X71) and quick respond (X72). TABLE IV. IDENTIFICATION OF IN FLIGHT MEAL SERVICES ATTRIBUTES Type Menu Variant Originality Appearance Suitability Cordiality Punctually Responsibility i X1 X2 X3 X4 X5 X6 X7 1 Vegetarian foreign country Unique Suitable Greeting quarter time earlier Slow respond 2 Moslem Old country Standard Different More information middle time Quick respond 3 Kosher quarter time finish In this paper, we use hypothetical data. Assumed that we are collecting 15 samples, and then we classify the sample based on type (categories each attributes) that be defined on table 4. And then, we evaluated their passenger’s preferences per each personality traits. Respondents that are used on this case as many 15 people per each personality traits. In other words, we totally collect 45 data passengers’ preferences by using semantic differential questioners. This questioner has range 1-7 (1 indicated unlike and 7 indicated like so much). The result of preferences from 15 respondents furthermore averaged. Data classify and evaluating of samples is shown on table 5. TABLE V. DATA EVALUATION OF SAMPLE SERVICE No sample CLASSIFICATION OF IN FLIGHT MEAL SERVICES PASSENGER’S PREFERENCES X1 X2 X3 X4 X5 X6 X7 Neophobia variety seeking selective variety seeking 1 2 1 1 1 1 2 1 2,324 4,802 1,524 2 1 2 1 2 1 1 2 2,336 4,666 4,696 3 3 1 2 1 2 3 1 2,937 6,504 4,158 4 1 2 2 1 2 2 2 5,280 4,298 5,016 5 3 1 1 2 1 3 2 4,706 6,747 3,014 6 3 1 2 1 1 3 1 5,146 6,153 4,216 7 2 2 1 1 2 2 1 2,259 2,801 1,757 8 1 2 1 1 1 2 2 1,479 4,926 3,333 9 3 1 2 2 1 3 1 4,939 1,572 6,558 10 2 2 1 1 2 1 2 2,431 5,703 5,187 11 3 1 1 1 1 2 1 5,014 6,277 5,832 12 3 2 2 1 1 3 1 1,012 3,297 6,161 13 3 2 2 2 2 3 1 3,569 1,230 2,965 14 3 2 1 1 2 3 2 6,296 6,302 2,815 15 2 2 2 2 1 3 1 4,503 1,125 5,546 Data as result from questioner became an input for formulating QTT1 on R software. By using QTT1, we design of in-flight meal services. On Figure 4 represented dashboard design formulation each passenger’s personality traits and table 6 are shown the coefficients each categories and PCC (Partial Correlation Coefficient) each attribute of in-flight meal services. From dashboard, we know rapidly the recommend- dation of developing the design formulation in flight meal that suitable with passenger’s personality traits. Every value in each category shows the influence level of attributes. We recommended the highest value of category score in each attribute. By implementation of this system, we developed in- flight meals services based on personality traits by real timely. Recommendations of design formulation are represented on table 7. Figure 3. Dashboard Design Formulation Each Passenger’s Personality Traits ICITEE 2014 Yogyakarta, 7-8 October 2014 ISBN: 978-1-4799-5302-8 -118-
- 19. TABLE VI DESIGN FORMULATION OF IN FLIGHT MEAL SERVICES EACH PERSONALITY TRAITS Attribute of In-flight meal services Neophobia Variety Seeking Selective Variety Seeking Categories PCC Categories PCC Categories PCC x1 x1.1 -2,136 0,480 0,877 0,430 -3,162 0,706 x1.2 0,011 -0,501 -0,567 x1.3 0,796 -0,079 1,470 x2 x2.1 0,514 0,262 1,456 0,864 -0,606 0,409 x2.2 -0,343 -0,971 0,404 x3 x3.1 -0,746 0,461 0,754 0,625 -1,865 0,824 x3.2 0,853 -0,861 2,131 x4 x4.1 -0,391 0,366 0,867 0,860 -0,044 0,068 x4.2 0,783 -1,735 0,088 x5 x5.1 -0,142 0,120 -0,113 0,184 0,843 0,706 x5.2 0,213 0,170 -1,265 x6 x6.1 -1,001 0,431 0,399 0,700 3,051 0,731 x6.2 1,165 -1,239 1,614 x6.3 -0,478 0,675 -1,772 x7 x7.1 -1,126 0,564 -0,529 0,486 -8,136 0,635 x7.2 1,689 0,793 1,220 Constant 3,615 4,427 4,185 R -0,477 0,741 0,214 R2 0,473 0,907 0,719 TABLE VII. DESIGN OF IN FLIGHT MEAL SERVICES EACH PERSONALITY TRAITS Design X1 X2 X3 X4 X5 X6 X7 Neophobia Kosher old country Standard different more information middle time slow respond variety seeking selective Vegetarian old country Unique Suitable more information quarter time finish quick respond variety seeking Kosher foreign country Standard different greeting quarter time early quick respond V. CONCLUSION Passenger’s personality traits have a powerful relationship with in-flight meal services. Our finding revealed the personality traits as neophobia, variety seeking selective, and variety seeking. Designed formulation of in-flight meal services are different both in menu variant, originality, appearance, suitability, cordiality, punctually and responsibility. Implementation of this systems support the airline business could contribute for decision making to formulate passenger’s requirement in flight meal services. Our recommandation are integreted this system with current booking and membership database systems. REFERENCES [1] Transportation departemen Indonesia, “Airline market in Indonesia was increasing rapidly”, Media Trans: Jakarta, 2012. [2] Archa R, and Subha MV, “A study on service quality and passenger satisfaction on indian airlines”, International Journal of Multidisciplinary Research, vol 2 issue 2. ISSN 2231 5780, 2012. [3] Janger JW and Dion VZ, “ailine service quality in south africa and malaysia –an internatioal customer expectation approach”, vol.5 no 11, 2013. [4] Feng CM and Kung YJ, “analysis airline service improvement strategy through importance and performance analysis”, journal of Eastern asia sosiety for transportation studies, vol 6, pp 782-797, 2005. [5] Upadhyaya M, “Customer satisfaction measurement in airline services: an empirical study of need –gap analysis”, International Journal of Management and Strategy, vol 3 issue .5, 2012. [6] Jones P. “Flight-Catering”, Behr’sVerlag: hamburg, 2007, p.39-55 [7] Bahraeni K, et al, “Measuring service quality and a comparative analysis in airline industry”, Management Science Letters, vol.3, pp 275–280, 2013. [8] Mak AHN, et al, “Factor Influencing tourist food consumption”, International Journal of hospital Management, vol 31, issue 3, 2012, pp 928-936 [9] Azvine B., Cui, Z., Nauck, D.D. & Majeed, B, “Real Time Business Intelligence for the Adaptive Enterprise,” The 3rd IEEE International Conference on Enterprise Computing, ECommerce, and E-Services, pp. 1-11, 2006 [10] Botan I., Cho, Y., Derakhshan, R., Dindar, N., Haas, L.M., Kim, K. & Tatbul, N, “Federated Stream Processing Support for Real- Time Business Intelligence Applications”, In BIRTE 2009, pp. 14-31, 2009. [11] Sandu I, “Operational and Real-Time Business Intelligence”, Revista Informatica Economică, no.3(47), pp. 33-36, 2009. [12] Popeanga, J and Ion L, “Real Time Business Intelligence for Utilities Industry”, Database systems journal, vol. 3, no.4, 2012. [13] Han J, and Kamber M, “Data Mining Concept and Techniques 2nd edition”, Elsevier: London, 2006. [14] Noriko IY, Xavier L, and David EG., “Discourse analysis and creativity support for concept product design”, 2009. [15] Hui, Shurong, et al, “the application of quantification theory 1 in site quality evaluation of japanese larch tree (larix kaemferi)”, international Journal of information and systems sciences, volume 5, number 3-4 pp 528-531, 2009. [16] Zhang Y, “Developing Effect of HTML5 Technology in web game,” International Journal on Computational Sciences & Applications (IJCSA) Vo2, No.6, December 2012. ISBN: 978-1-4799-5302-8 Yogyakarta, 7-8 October 2014 ICITEE 2014 -119-