Desain Afektif Untuk Kemasan Asinan Bogor

AN AFFECTIVE DESIGN FOR BOGOR PICKLE
PACKAGING

NOVI PURNAMA SARI

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015

STATEMENT OF THESIS AND SOURCES OF
INFORMATION AND DEVOLUTION COPYRIGHT
Hereby, I declare that the thesis entitled “An Affective Design for Bogor
Pickle Packaging” is my own work under supervision of Dr Eng Taufik Djatna STP
MSi and Dr Mirwan Ushada STP M App Life Sc. It has never been previously
published in any university. All of incorporated originated references from other
published as well as unpublished papers are stated clearly in the text as well as in
the references.
Hereby, I devolve the copyright of my thesis to Bogor Agricultural University.
Bogor, July 2015

Novi Purnama Sari
Student ID F351130221

SUMMARY
NOVI PURNAMA SARI. An Affective Design for Bogor Pickle Packaging.
Supervised by TAUFIK DJATNA and MIRWAN USHADA.
Currently most of Bogor pickle packaging are poorly designed and out of
customer needs for a pleasurable product. The pickles have become one of the icon
of Bogor since 1970, however until now there was never development of pickle
packaging design. According to earlier market survey on fifty customer known that
information of complaint on pickled product is more packaging factor. So that the
packaging design process is very important to improve the performance and
appearance of the product. Performance is the overall quality of the product, while
the appearance of the design is the outside form which visible and can support
attraction of customer to buy product. Performance and appearance are factor to
create success product. Beside that another factor are make priority of customer
pleasure and determine of market segmentation. Pleasure factor becomes important
because it can lead to comfortable emotions, pride, fun, and joy of customer when
they are interacting with the packaging. The approach of Kansei Engineering (KE)
is used to be solution, because KE is capable to effectively describe the emotions

of customer into product design elements. As an effort to satisfy those requirement,
the contributions of this paper are 1) To select the design concept appropriated
correction to the customer Kansei; 2) To determine the design elements based on
the proposed design concept; 3) To integrate and evaluate the proposed model of
design concept. The user preference which reflect the affective design requirement
are applied on certain customer segmentation. Affective design information and
knowledge are acquired as Kansei Word (KW) as verbal part of Kansei Engineering
(KE) approach. The segmentation was ruled out by Pillar K-means algorithm. A
Principal Component Analysis (PCA) formulation produced design concept using
KW, which followed by Quantification Theory Type 1 (QTT1) analysis to correlate
packaging design elements with the previously generated design concepts. These
steps previously generates design a fully 3D affective designed for the packaging.
In order to infer the design, an Interval Type 2 Fuzzy Sets (IT2FS) was implemented
to support design inquiry for each packaging evaluation.
The result of market segmentation is specialty food gift and focus for pleasure
needs. Forty pairs of Kansei word were collected by interview to get design
concepts. Two design concepts selection are generated using PCA analysis:
"Standard-Attractive" and "Trendy-Classic", however, "Standard-Attractive"
concept has R-square value greater than "Trendy-Classic" concept. So it is chosen
as the target output in interval type 2 fuzzy sets in the next stage. Six design

elements were identified from morphological analysis to twenty seven packaging
samples as input data. According to the result of QTT1, a linear quantitative model
has been built to analyze the relationship between customer impression on
Attractive concept and relevant six design elements with considered specialty food
gift and pleasurable of product. The results of QTT1 analysis specification design
elements of “Attractive” concept are: Top Shape is Concave curve (X1.1), Body
Shape is Jaggy bowl curve (X2.4), Bottom Shape: Line (X3.2), Lid Shape is Artistic
(X4.6), The Volume is Large (X5.3), and Design Label is Informative (X6.1). This
design has an element of local wisdom to support consumer habits in consuming

pickled by togetherness. The element is supported by the shape of the design such
as bowls and large. The model was concluded to ease the user in evaluating based
on the highest consistency (95.45%) for predicting the value of the StandardAttractive image. For further researches are developed a model packaging design
by considering customer needs requirement based on emotional non-verbal verb
measurement to include for the whole evaluation model using Kansei engineering
Keywords: An affective design, pickle packaging, Kansei engineering, PCA,
QTT1, IT2FS

RINGKASAN
NOVI PURNAMA SARI. Desain Afektif untuk Kemasan Asinan Bogor.

Dibimbing oleh TAUFIK DJATNA dan MIRWAN USHADA.
Saat ini sebagian besar desain kemasan asinan Bogor memiliki desain yang
buruk dan belum memenuhi kebutuhan konsumen. Sejak tahun 1970 asinan telah
menjadi salah satu icon Kota Bogor, namun sampai saat ini tidak ada
pengembangan desain kemasan asinan yang menarik. Menurut survei pasar
sebelumnya pada lima puluh konsumen diketahui bahwa informasi dari keluhan
terhadap produk asinan adalah lebih ke faktor kemasan. Sehingga proses desain
kemasan sangat penting untuk meningkatkan performa dan penampakan dari
produk. Performa adalah kualitas keseluruhan produk, sedangkan penampilan
desain adalah bentuk luar yang terlihat dan dapat mendukung daya tarik pelanggan
untuk membeli produk. Performa dan penampakan adalah faktor menciptakan
produk yang sukses. Selain itu faktor lainnya adalah mengutamakan pleasure
konsumen dan menentukan segmentasi pasar dari produk. Faktor pleasure menjadi
hal yang penting karena mampu menimbulkan emosi nyaman, bangga, dan rasa
senang dari konsumen. Pendekatan rekayasa Kansei digunakan untuk menjadi
solusi, karena KE mampu untuk secara efektif menggambarkan emosi konsumen
menjadi elemen-elemen desain produk. Sebagai upaya untuk memenuhi kebutuhan
mereka, kontribusi dari makalah ini adalah pertama untuk memilih konsep desain
berdasarkan Kansei konsumen, kedua untuk menentukan elemen desain kemasan
berdasarkan konsep desain, dan ketiga untuk mengintegrasi dan evaluasi model

yang diusulkan dari konsep desain. Preferensi pengguna yang mencerminkan
kebutuhan desain afektif diterapkan pada segmentasi pelanggan tertentu. Informasi
desain afektif dan pengetahuan yang diperoleh sebagai Kansei Word (KW) sebagai
bagian dari verbal pendekatan Kansei Engineering (KE). Segmentasi diatur oleh Kmeans pillar algoritma. Principal Component Analysis (PCA) formulasi
menghasilkan konsep desain menggunakan KW, yang diikuti oleh analisis
Quantification Theory Type 1 (QTT1) untuk mengetahui korelasi elemen desain
kemasan dengan konsep desain yang dihasilkan sebelumnya. Langkah-langkah ini
sebelumnya menghasilkan desain afektif sepenuhnya 3D yang dirancang untuk
kemasan. Dalam rangka untuk menyimpulkan desain, Interval Type 2 Fuzzy Sets
(IT2FS) diimplementasikan untuk mendukung penyelidikan desain untuk setiap
evaluasi kemasan.
Hasil segmentasi pasar adalah makanan khusus oleh-oleh dan fokus pada
kebutuhan kesenangan (pleasure). Empat puluh pasang kata Kansei dikumpulkan
dengan wawancara untuk mendapatkan konsep desain. Dua konsep desain pilihan
yang dihasilkan dengan menggunakan analisis PCA: "Standar-Menarik" dan
"Trendy-Classic", namun, "Standar-Menarik" konsep memiliki nilai R-square lebih
besar dari "Trendy-Classic" konsep. Jadi konsep ini dipilih sebagai target output
dalam Interval Tipe 2 Fuzzy Sets (IT2FS) dalam tahap berikutnya. Enam elemen
desain yang diidentifikasi dari analisis morfologi dengan dua puluh tujuh sampel
kemasan sebagai data masukan. Menurut hasil QTT1, model linear kuantitatif telah

dibangun untuk menganalisis hubungan antara kesan pelanggan pada konsep
Menarik dan elemen enam desain yang relevan dengan mempertimbangkan
makanan khusus oleh-oleh dan produk yang menyenangkan (pleasurable). Hasil

analisis QTT1 spesifikasi elemen desain untuk konsep "Menarik" adalah: Bentuk
atas: kurva cekung (X1.1), Bentuk badan: kurva mangkok bergerigi (X2.4), Bentuk
bawah: garis (X3.2), Bentuk tutup: artistik (X4.6), Volume: besar (X5.3), dan Desain
label: informatif (X6.1). Desain ini memiliki unsur kearifan lokal untuk mendukung
kebiasaan konsumen dalam mengkonsumsi acar oleh kebersamaan. Unsur ini
didukung oleh bentuk desain seperti mangkuk dan besar. Model ini disimpulkan
untuk memudahkan pengguna dalam mengevaluasi berdasarkan konsistensi
tertinggi (95,45%) untuk memprediksi nilai gambar Standar-Menarik. Untuk
penelitian lebih lanjut mengembangkan model desain kemasan dengan
mempertimbangkan kebutuhan pelanggan persyaratan berdasarkan pengukuran
kerja non-verbal emosional untuk menyertakan untuk model seluruh evaluasi
menggunakan rekayasa Kansei
Kata kunci: Desain emosional, kemasan asinan, rekayasa Kansei, PCA, QTT1,
IT2FS

© Copyright 2015 by IPB

All Rights Reserved
No Part or all of this thesis may be excerpted without or mentioning the sources.
Excerption only for research and education use, writing for scientific papers,
reporting, critical writing or reviewing of a problem. Excerption doesn’t inflict a
financial loss in the paper interest of IPB.
No part or all of this thesis may be transmitted and reproduced in any forms without
a written permission from IPB.

AN AFFECTIVE DESIGN FOR BOGOR PICKLE
PACKAGING

NOVI PURNAMA SARI

Thesis
as partial fulfillment of the requirements for the degree of
Master of Science
in the Agroindustrial Technology Study Program

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY

BOGOR
2015

External examiner: Prof Dr Ir Rizal Syarief Sjaiful Nazli, DESS

Thesis Title : An Affective Design for Bogor Pickle Packaging
Name
: Novi Purnama Sari
Student ID : F351130221

Approved by
Supervisor

Dr Eng Taufik ｄｪｾｴｮ｡Ｌ＠
Chaim1an

STP MSi

Dr Mirwan U sh
Me

Acknowledged by

Head of
Agroindustrial Technology
Study Program

Prof Dr Ir Machfud, MS

Dr Ir Dahrul Syah, MSc Agr

Examination date:
July 29 111 2015

Passed date:

3 1 AUG 2015

PREFACE
I would like to thank Allah Subhanahu Wa Ta’ala for all His gifts so this

research is successfully completed. The theme chosen in the research which
conducted during February 2014 is product development, with the title of An
Affective Design for Bogor Pickle Packaging.
I would like to express my sincere gratitude to Dr Eng Taufik Djatna STP
MSi as Chairman of Advisory Committee for his support and encouragement during
my study in Bogor Agricultural University. I am very grateful to Dr Mirwan Ushada
STP M App Life Sc as Member of Advisory Committee for his advice, supervision
and taking the time during the thesis work. I also do not forget want to say thank
you for Prof Dr Ir Rizal Syarief Sjaiful Nazli DESS as my Examiner who has helped
me and Dr Ir Titi Candra Sunarti MSi as my moderator. I would like to say great
thanks to my family Johari (father) and Istikana (mother), Hj. Andjar Astuti (mother
in-law), H. Mohammad Epa Komala (father in-law), Alm. Nurvia Setiati (sister),
Ahmad Thoriq (brother), Any Hadyastuti (sister), and all my family-in-law for their
prayers and support. Beside that a great thank you to my beloved husband
Mohammad Septa Ayatullah for his true and endless love, you are my everything.
I would like to thank all lecturers and staff of Agroindustrial Technology
Department, all of my best friend in Agro-industrial Technology 2013, my
colleagues in Sensei’er Community and all my best friends off campus “Elfira
Febriani, Hetty Handayani Hidayat, IB Dharma Yogha, Nisa Alifa, Elfa Susanti
Thamrin, Mrs Rahmawati, Mr Azri Firwan, Riva Aktivia, Nina Hairiyah, Aditya

Ginantaka, Lely Rachma Septiana, M. Zaki Hadi, Rohmah, Fajar Munichputranto,
Husnul Khotimah, Mrs Puspa, Aisyah, Yudhis, Ikhsan, Imam, Denny, Septian,
Latindfarend, Tifanescibulphy” for their support. I would like to grateful Ministry
of Education that have provided me with scholarship enable for this master courage
completion.
Hopefully this thesis is useful.

Bogor, July 2015
Novi Purnama Sari

TABLE OF CONTENTS
TABLE OF CONTENTS

viii

LIST OF TABLES

ix

LIST OF FIGURES

ix

LIST OF APPENDIX

ix

1 INTRODUCTION
Background of Research
Problem Statement of Research
Objective of Research
Benefit of Research
Boundaries of Research

1
1
3
4
4
4

2 METHODOLOGY
Research Framework
Determination Domain of Customer
Selection Design Concept
Determination Design Elements
Synthesis Design of Packaging Using Quantification Theory Type 1
Building the Model Using Interval Type 2 Fuzzy Sets

4
4
6
6
8
8
8

4 RESULT AND DISCUSSION
Domain of Customer
Aspect of Customer Needs
Design Concepts
Design Elements
Design of Packaging
The Model Evaluation

10
10
11
13
18
20
24

5 CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation

29
29
30

REFERENCES

30

APPENDIXES

32

GLOSSARY

50

BIOGRAPHY

52

LIST OF TABLES
1
2
3
4
5
6
7
8
9
10
11
12

Identification of customer needs for pickles packing
12
Data set of Kansei word
15
The value of variances and proportion of variances
16
Numerical data source for the twenty seven representative packaging
samples
18
Morphological Analysis of the twenty seven representative samples of
packaging
19
Value of category grade and partial correlation coefficient each design
element
21
Input and output in IT2FS model
24
Triangular fuzzy numbers for the top shape (X1) form element
26
Triangular fuzzy numbers for the packaging image (Y)
26
Membership function formulation for top shape
26
Fuzzy rules for determining the S–A value of pickle packaging
27
The RMSE results of the IT2FS
29

LIST OF FIGURES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

Level of customer complaints about Bogor pickle (Kartasasmita 2014) 1
A hierarchy of consumer needs (Jordan 200)
2
A proposed Model on Kansei Engineering (Schütte 2006)
3
An affective design framework of Bogor pickle based on KE
5
Example of semantic differential questionnaire with 7-point scale
6
Membership function for fuzzy type 2 (Mendel et al. 2006)
9
Stages of development IT2FS (Mendel et al. 2006)
9
The good cluster solution without average negative silhouette score 10
Plot results of cluster market segmentation
11
The four pleasures (Jordan 2000)
11
Tools for support stimulate respondents
13
Representation of twenty seven packaging samples
14
Plot of variances each principal component (PC)
15
Plot of PC1 and PC2 weight distribution
17
QTT1 bar graphic score category of standard-attractive concept
20
Representation 2D and 3D image of QTT1
22
Representation of informative design label
23
The triangular MF of input (X1 to X6) and output (Y)
25
User profile interface of model evaluation program
28
Operation interface of model evaluation program
28

LIST OF APPENDIX
1
2
3
4

Profile of respondent
Data identification of market segmentation
Market segmentation questionnaire
Introduction questionnaire

32
34
35
36

5
6
7
8
9
10

Packaging samples
Identification of packaging design elements questionnaire
Evaluation Kansei word questionnaire
Data of Kansei word evaluation
Representation image of label on design packaging using QTT1
Member function specialty food gift product

38
40
42
44
45
46

1

1 INTRODUCTION
Background of Research
Currently customer of Bogor pickle suffer from poorly and out of requirement
in cause lack of appearance and affective design. Our recent survey should that on
twenty sellers, only three sellers which provide cup packaging, while most of the
sellers are just using simply packaging. Based on interviews of fifty customers in
Figure 1 showed that customer complaints are more to the packaging factor than
the content itself (Kartasasmita 2014). Majority of complaints are: high risk of
leakage, non-hygiene packaging, not ergonomic, unattractive design, and short
shelf life of the product. According to the Ministry of Industry and Trade of Bogor,
a pickle is one of the traditional foods that has been become an icon of Bogor since
the 1970. Thus this is an opportunity to improve the correct packaging design.

Figure 1 Level of customer complaints about Bogor pickle
Source: secondary data (Kartasasmita 2014)
According to Jordan (2000) claimed that a new approach to human factors is
about fitting products to people in a holistic manner, because the usability quality
of the relationship between people and products depends on more than simply
product but pleasure factors of customers. Taking the idea of a hierarchy of needs
and applying it to human factors, the model illustrated in Figure 2 is proposed by
Jordan. It is intended to reflect the way that the contribution of human factors to
product design might be seen-either explicitly or implicitly-by both manufacturers
and those who buy and use their products.
Pleasure-based approaches are about really understanding people and
respecting and about designing products that can bring a real joy into people’s lives.
Now, in recent years success of a well-designed product not only depends on the
functional requirements alone. Pleasurable need has four types of concepts:
physical, psychological, social, and ideological (Jordan 2000). The customers’

2
psychological needs i.e. perceptual and emotional experiences also need to be
fulfilled (Sharma et al. 2013). Hariyadi et al. (2002), has conducted research based
on functionality need by the modification of the packaging in the cup pickled using
a thermal process technologies to enhance the shelf life of the product. On the other
hand, Kartasasmita (2014) doing business development of Bogor pickled by
creating packaging innovative more safe, practical, hygienic, informative to meet
usability needs of customer. However, there has been no research on the design of
pickles packaging that focus on the pleasure needs based on Kansei. Research on
Kansei engineering to pickles product ever conducted in Japan by Endo et al. (2006,
2008) to analyze the image impression content of pickle product.

Figure 2 A hierarchy of consumer needs (Jordan 2000)
Basically, in product development, there are a lot of conventional and
classically approach such as: Quality Function Deployment (QFD), Conjoint
Analysis, and Voice of Customer (VoC). The method mentioned above generally
focus on implicit needs of customer, while Kansei Engineering method is one
method to analyze the needs of the customer implicit to produce the affective design
(Lokman 2010). affective design is called as an emotional, expression, or
impression. So that the affective design associated with Kansei. Kansei is the
feeling felt by the receiver of stimuli contained in the atmosphere of a situation.
There are various forms of emotion that can be expressed, either verbal or nonverbal such as brain waves, functional magnetic resonance imaging (FMRI),
Nuclear Information and Resource Service (NIRS), electromyography from the
body, galvanic skin response (GSR), eye movement, face expression, words, and
attitude behavior. The boundary in this research to understand the implicit
information on verbal Kansei and to translate into element in product design.
Schütte (2006) examined different types of Kansei Engineering and
developed a general model covering the contents of Kansei Engineering, as follows:
First, determine of domain that includes the selection of a target group of people,
market segmentation, product samples are collected, and representing the domain.
Market segmentation is determined using the Pillar K-means algorithm. Second,
the span the semantic space, to collect a large number of word describing the
domain (Kansei word). The design concepts based on Kansei word are extracted
using feature extraction methods like Principle Component Analysis (PCA) (Barnes

3

et al. 2008). Third, the span the space of properties to collect products representing
the domain from existing product, identifies key features and selects product
properties for further evaluation. Fourth, the synthesis, the probably most important
step, which makes Kansei Engineering unique is its ability to connect the describing
words (Kansei words) with the properties of the product. Hsiao et al. (2010) claimed
in his research that the process of designing new product will be more effective and
efficient by using QTT1. And the last is building the model. Figure 3 portrays this
framework.

Figure 3 A proposed Model on Kansei Engineering (Schütte 2006)
These models are a function depending on the product properties and predict
the Kansei score for a certain word (Sharma et al. 2013). Based on the study (Lin
et al. 2007) Fuzzy Logic is compatible design model to describe the relationship
between the shape of the product (as a variable input) and consumer perception (as
a variable output), where consumer perceptions are often expressed subjectively
and it is uncertainty or has a grey area. In particular fuzzy logic methods provide an
effective framework for modeling human feelings as Kansei words in decision
making. The concept of Type-2 fuzzy sets (IT2FS) was proposed by Zadeh in 1975,
as an extension of the concept of Type-1 fuzzy set to combine uncertainties of
consumer perceptions. IT2FS methods believed more powerful to improve
deficiency of Fuzzy type 1 methods (Mendel and Wu 2010).
Problem Statement of Research
As the problem definition above, the formulation of the problem in this study
as follows:
1. How is design concept based on customer Kansei?
2. What are design element based on design concept?
3. How to evaluate system?

4
Objective of Research
The objectives of this research are 1) To select a design concept appropriated
correction to the customer Kansei; 2) To determine the design elements based on
the proposed design concept; 3) To integrate and evaluate the proposed model of
design concept.
Benefit of Research
The benefits of this research is to provide design recommendations packaging
pleasurable based on customer to everyone who wants to start a pickle business.
Furthermore our proposed design evaluation system potentially ease the user
evaluate the packaging design by using the Java programming language.
Scopes of Research
Scopes of this research includes the object of research which is pickles
packaging with a focus on market segmentation and pleasurable needs
predetermined based on Kansei Engineering approach. The method used in this
study include Pillar K-means algorithm, PCA, QTT1, and IT2FS. Research will be
conducted in Bogor which it is the main production center of pickled products. In
this study, the sampling of respondents performed on several panelists chosen by
purposive and judgment sampling.

2 METHODOLOGY
Research Framework
In the research framework described step-by-step of processes. Framework in
this study is based on a model in Kansei engineering developed by Schutte (2006).
The overall framework details are presented in Figure 4. The first step is
determining domain of customer in this research, by identifying the market
segmentation of Bogor pickle, after then is identifying aspect of customer needs.
Next is spanning the semantic space, it call as selecting design concept by collecting
Kansei words from expert panelist, evaluating Kansei word based on packaging
samples by semantic-differential questionnaire, and extracting Kansei word using
PCA method. The third is spanning the space of properties, it call as determining
design elements by collecting packaging samples of existing product, and then
identify its design elements using morphological analysis technique. The fourth step
is synthesis design of packaging or a combine process between design concepts and
design elements using QTT1 method, and the last step is building the model to
integrate and evaluate the proposed model of design concept.

5

Determining Domain of Customer
Start
Identifying market
segmentation
[Preface Ques. 1]
Cluster analyszing by
Pillar K-means
Algorithm
Market
Segmentation
Identifying aspect of
Customer needs
[Preface Ques. 2]
Customer
needs
Determining Design Elements
Sellecting Design Concept

Collecting the pckaging samples
[Market survey]

Collecting the Kansei word
[Impression Ques.]

Samples

Evaluating the Kansei word
[Semantic-Differential
Ques.]

Identifying of design elements
in the samples
by Morphology Analysis
[Identify-elements Ques.]

Extracting Kansei
word by PCA

Design elements
Design concept
Evaluating design concept
[SD-concept Ques.]

Tabulating samples based
on design elements
Synthesis Design of
Packaging

Tabulation of data

Correlation analyzing of
design element and
concept design by QTT1
Design of packaging
Building the Model
Determining input and output
Determining the membership
functions of input and output
Building the fuzzy rules by IT2FS
Evaluating performance of model
Model evaluation

Note :
= Flow of information from
customer
= Flow of information from
expert panelists
= Flow of information
from result processed in
researchers

Figure 4 An affective design framework of Bogor pickle based on KE

6
Determination Domain of Customer
a. Identification the market segmentation using pillar K-means algorithm
The market segmentation of products are identified to determine the domain
of customer in the research by Pillar K-means algorithm. Barakbah and Kiyoki
(2009) claimed that Pillar K-means algorithm to ensure optimize the selection of
initial centroids and improve the K-means precision in all data sets and in most
of validity measurements. The algorithm is inspired by the function of pillars of
a building or a construction. It is common that a pillar in a building is deployed
at each edge or at each corner in a building, so that the mass of the building is
concentrates in each pillar. The same idea is adopted for the clustering task that
the best initial centroids are presumed exist in the edge of the dataset, or in other
word, those k-furthest objects in the dataset is selected as initial centroids, where
k is number of cluster to be observed.
b. Identification aspect of customer needs
Consumer needs are identified by in-depth interviews on five prominent
expert panelist in the field of packaging to determine the aspects of the
functionality need, usability need and pleasurable need. In addition, this
argument is strengthened by the study of literature (Klimchuk and Krasovec
2012).
Selection Design Concept
a. Collection Kansei words
Kansei words are collected by in-depth interviews on five prominent expert
panelist in the field of packaging and customer. In-depth interviews process
using mentality constraint technique proposed by Ushada and Murase (2009).
Mentality constraint technique is a technique to stimulate respondents by support
tools, namely: catalogues about packaging, samples of packaging, and
questionnaire.
b. Evaluation Kansei word using semantic differential (SD) questionnaire
Nagasawa (2002) claimed that physiological measures, which are measures
of physiological responses, behaviors, and body expressions generated by
“external stimulation”, while psychological measures is the semantic differential
scales method (SD method). The Semantic Differential introduce by Osgood et
al in 1957. SD- Scales are a political instrument for measuring the affective
impact of political streams on the citizen’s mind. This tool can also be used in a
modified version for product development. For examples of semantic differential
(SD) questionnaire is shown in Figure 5 as following (Nagamachi and Lokman
2011).
-3

-2

-1

0

1

2

3

Classic

Trendy

Uncomfortable

Comfortable

Unattractive

Attractive

Figure 5 Example of semantic differential questionnaire with 7-point scale
Osgood uses 7 point Semantic Differential Scales gathering for evaluation.
A 7-point scale allows more sensitive ratings, while it is as comprehensive and
quick to use as a 5-point scale (Schutte (2005)

7

c. Extraction Kansei word using principal component analysis (PCA)
The feature extraction was done to extraction Kansei word became the
concept design. In this step we were using PCA method, because PCA is a
statistical technique to extract the information of a large set of correlated
variables into a few principal components, without reduce to the meaning of the
variability present in the data set (Dai et al. 2011). It is also as implied by
Bouzalmat et al. (2014) that PCA is a powerful tool for feature extraction with
main advantage could reduce the dimension of the data without losing much
information. We could determine concept design based on principal component
that have been chosen. The result of respondents assessed the visual of packaging
samples based on Kansei word by semantic differential questionnaire will be
calculated using R programming language.
To deploy PCA in this research, firstly, defines Q as a matrix (n x m) of
Kansei evaluation data for n-dimensions of Kansei word which denotes as Q =
{q1, q2…qn} for m samples in evaluation, where qn is Kansei word to-n. The aim
of PCA in Kansei engineering is to obtain a linear combination of variables that
summarizes an n-dimensions distribution (e.g., n =∑ �
��
, n = 80),
using a lower-dimensional space (Nagamachi and Lokman 2011). Step-by-step
of principal component analysis were as follows (Jatra et al. 2007):
1. Compute mean of Q matrix in eq. 1
1 n
q   qi
(1)
n k 1
2. Subtract Z matrix in eq. 2



Z  q1  q, q2  q,..... qn  q

3. Determine variance in eq. 3 and covariance in eq. 4
1 m
Var ( x)   (q j  q)2
m j 1

C

1
Z k * Z kT
(m  1)

(2)

(3)
(4)

4. Compute eigen value and eigen vector of covariance in eq. 5.
C.e  .e
(5)
where e is eigen vector and λ is eigen value
5. Calculate the variance proportion of each PC and accumulated value for PC-q.
A measure of how well PC-q able to explain the variance is given by the
proportion relatively in eq. 6
g

g 


j 1
m


j 1

j

(6)
j

Where ψq proportion of the variance, λj is eigen value to-j, g is PC to-g, and m
is the number of packaging samples.

8
Determination Design Elements
a. Collection the packaging samples
Packaging samples are collected by observation of the whole work via survey
market and internet. After then packaging samples are selected by analyzing with
five prominent expert panelist in the field of packaging.
b. Identification of design elements
Each of packaging samples later will be identified their design element and
their type of design element using morphological analysis technic using
questionnaire. This method to analyze the physical similarity of the sample, so
design elements of the packaging can determined (Wei et al. 2011). Klimchuk
and Krasovec (2012) claimed that design elements of packaging include to the
primary display panel, typography, color, imagery, structure, materials, and
sustainability, production, legal and regulatory issues.
Synthesis Design of Packaging Using Quantification Theory Type 1
On the application of Kansei Engineering synthesis process to determine the
relationship between Kansei word and design elements could use the Quantification
Theory Type 1 (QTT1) method (Schütte and Eklund 2001). According to Hsiao et
al. (2010) the process of designing new product will be more effective and efficient
by using QTT1. It is also as implied by Nagamachi and Lokman (2011) that QTT1
is one tool of multiple linear regression analysis to quantify the relationship
between the design element and design concept. This method could change the type
categorically of independent variables (design elements) into the type quantitative
in Kansei Engineering. So QTT1 method is an excellent method to be applied. The
formulation of model in eq. 7 as follows QTT1 (Lai et al. 2006):
^n

E

Cs

y m    st X stm  

(7)

s 1 t 1

^n

Where y m is the predicted value of the variable standard for all product
samples to-m on Kansei word, s is the index of the design elements, E is the number
of design elements, t is the index of categories, Cs is the number of categories of
design elements to-s, ε is a stochastic variable (error), βst is the value category to-t
with design element to-s, Xstm is the coefficient of the dummy variable of categories
to-t, design element to-s on sample product to-m.
Building the Model Using Interval Type 2 Fuzzy Sets
In IT2FS has a limited area that contains the primary uncertainty membership
degree of membership function type 2 called A footprint of uncertainty (FOU).
Upper membership function (UMF) and lower membership function (LMF) be the
limit of FOU, as an illustration in Figure 6. UMF is the upper limit of FOU while
LMF is a lower limit of FOU. In general, the process steps IT2FS are shown in
Figure 7 (Mendel et al. 2006):

9

Figure 6 Membership function for fuzzy type 2 (Mendel et al. 2006)

Figure 7 Stages of development IT2FS (Mendel et al. 2006)
Step by step explanation of the method IT2FS is:
a. Design of fuzzifier
Membership Type Function (MF) used are triangular functions, input
variables obtained from the identification of Kansei word. FOU formula in eq.
8 as following:
(8)
FOU ( A)  x'X J x'   x' , u  : u  J x  0,1





b. Construction of fuzzy rule
After a membership function of IT2FS are defined, the next step is to
establish rules for processing the input fuzzy using formulation in eq. 9 as
following:

R p : IF x1 is A1p and ...and xi is Alp , THEN b is B p , p  1, 2,..., P (9)
̃� is antecedent, Bp is consequent on fuzzy rules, x1....xi is input to
where �
1

the fuzzy, and y is the output fuzzy to be used in the fuzzy inference
c. Design of fuzzy inference
Results of the process fuzzification become input to IT2FS, and then it will
be done inference by calculate firing interval based on rules set using eq. 10 as
following:
p,
F p  x'    p  x1   ...  p  x1  , p  x1   ...  p  x1    f f p  (10)

10

where Fp(x’) is firing level to data set, ̅� is firing level to UMF, � is
firing level to LMF.
d. Type-reduction
Output of calculation firing interval is still IT2FS, therefore the next step is
to type reduction becomes IT1FS using eq. 11 and eq.12 as following:
�

=

and
�

=

� �
∑�
�=1 �� ��

(11)

� �
∑�
�=1 �� ��

(12)

�
∑�
�=1 ��

�
∑�
�=1 ��
where �� , ��

is firing level that correct to �� and �� in rule to-i that will be
maximized by �� and minimized by �� .
e. Defuzzifier
Defuzzifier is the last step to get the final result. Defuzzifier method
commonly used is the centroid method with eq. 13:
b b
(13)
b  x  l r
2
where b(x) is result of defuzzifier, bl and br is result of reduction type for
each lower and upper.

3 RESULT AND DISCUSSION
Domain of Customer
According to Schütte (2006), the first step to determine the domain is
identifying the market segmentation by in-depth interview on thirty four respondent.
Profile of respondent, as provided in Appendix 1, while data result of in-depth
interview is given in Appendix 2, and market segmentation questionnaire is given
in Appendix 3. Before the centroid of each clusters are determined, the optimal
value of α and β must be determined by trial and error. The values of α and β play
significant role in silhouette score. Silhouette function was to understand how good
an object is placed in a cluster (Barakbah and Kiyoki 2009). The best cluster
solution is only selected if it has no empty cluster and if it has no negative average
silhouette score as shown in Figure 8.

11

Figure 8 The good cluster solution without average negative silhouette score
Based on calculation, the best cluster solution were α = 0.1 and β = 0.2
because it had the highest silhouette score (s = 0.833938272) and value of centroids
were 1; 1.75; 7.5; 7.75. Three clusters of Bogor pickle were determined, are: supply
product for HORECA (hotel, restaurant, and cafe) (C1), specialty food gift (C2),
and instant product (C3). In The Figure 9 shows that second cluster (C2) has the
largest cluster number, so in this research we focus to develop the Bogor pickle
packaging for specialty food gift segmentations. This is similar with previous
research conducted by Kartasasmita (2014) that 92% of respondents agreed Bogor
pickle is a specialty food gift.

Figure 9 Plot results of cluster market segmentation
Aspect of Customer Needs
Identification of customer needs were conducted by discuss with prominent
expert panelists in psychological field of customer and packaging using point C on
introduction questionnaire, as provided in Appendix 4. The results obtained are

12
shown in Table 1, there are three important elements of customer needs to fulfilled
and one of them is focus on pleasurable need. Pleasurable need has four types of
concepts: physical, psychological, social, and ideological, as an illustration in
Figure 10 (Jordan 2000). Information of customer needs are necessary as the basis
for the development of packaging designs in future.

Figure 10 The four pleasures (Jordan 2000)
Table 1 Identification of customer needs for pickles packing
Packaging as container products
Functionality
Packaging as protective
need
Safe packaging used are made from PP plastic,
food grade, heat resistant and not susceptible
Usability
migration
need
Hygienic packaging with sterilization process
Packaging size according to the functional of the
product so that it’s easy to use
Packaging fitted with label
Physical
The form of packaging comfortable to held
Pleasurable
Large packaging added by handle
need
The handle of packaging with good dimension and
comfortable to held
Lid of packaging easy to open
Packaging can be easy to carry
Psychological Packaging easy to use
Packaging must be sealed before closed
Packaging is joyful
Basic color of packaging is transparent so that
customer can see the product contents
The label color is bright consider to product
characteristics (orange, red, yellow, green)
Typography is clear, legible, and modern
Clear information on the label consists of: expired
date, production date, composition, nutrition,
name of company, address company, brand,
volume, number of BPOM, halal label, product
storage information, and pickled product
illustrations

13

Symbol of product brand attractive and easy to
remember
The packaging gives a sense of pride to someone
when using
Social
Packaging labels provide tourist information to
support social activities such as interacting with
local people, meet other people in a tourist spot,
and do social events
Packaging enables consumers to establish social
relationships with family and friends through
pickle food gift
Packaging materials using food grade materials
Ideological and easily recyclable materials
Design Concepts
In this research, twenty seven samples of packaging (m) were selected by
expert panelist is shown in Figure 12 or Appendix 5. These samples collected based
on my observation of the whole world via Internet and direct market survey. The
packaging samples consider to the similarity of shapes, packaging materials,
packaging flexibility, and strength of lid. The material of sample was a plastic
because the non-plastic material was not recommended to use on characteristics of
Bogor pickle by the expert panelists. In this case, the Polypropylene (PP) was
chosen as a packaging material because considered the best quality. Mentality
constraint technique is a technique to stimulate respondents by support tools as
given in Figure 11. Figure 11 shows some tools such as catalog, packaging samples,
questionnaire, and BPOM regulation. Forty pairs of Kansei word (n) were selected
is shown in Table 2.

Figure 11 Tools for support stimulate respondents
The expert panelists chosen are they who a minimum of more than ten years
of experience in the field of packaging. They are a lecture in college. Respondents
in the research are twenty female and four male customers who ever buy a pickle,
aged fifteen to forty years old. The respondents were selected by purposive and

14
judgment sampling technique. This sampling method was chosen to get correct
information about the complaint as well as customer needs for Bogor pickle. Each
Kansei word were evaluated using semantic differential (SD) questionnaire with 7point scale. In evaluating a subject using opposite words. For examples of semantic
differential (SD) questionnaire is shown in Appendix 6 as following (Nagamachi
and Lokman 2011).

14

14

Figure 12 Representation of 27 packaging samples

15

Table 2 Data set of Kansei word
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

Kansei word
Trendy - Classic
Cozy - Uncomfortable
Plus illustrations - Without illustrations
Safe - Dangerous
Modern - Traditional
Unique - General
Simple typography - Complex typography
Nice - Poor
Atrractive - Not Atrractive
Bright - Pasty
Colorful labels- Plain labels
Hardy - Brittle
Practical to use- Difficult to use
Complex -Simple
Exotic - No exotic
Functional - Not functional
Flexible - Rigid
Easy - Difficult
Transparent - Opaque
Large - Small

No
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

Kansei word
Khas - Common
Innovative - Conservative
Aesthetics - Natural
Artistik - Not artistic
Protection - Not protection
Informative - Not informative
Communicative design - Uncommunicative design
Characteristic - Not characterized
Eyechatching - Bright color
Clearly label - Faded label
Identity - Without identity
Describe the product - Not describe the product
Liked - Disliked
Pleasure - Not pleasure
Labels can promote - Labels can not promote
Matching - Not applicable
Different - Not different
Luxurious - Standard
Ergonomic - Not ergonomic
Glory design - Disappointing design

Variances

Principal components (PC) are computed based on evaluation data of forty
pair Kansei words (n) using semantic differential questionnaire contained twenty
seven parameters (a), as provided in Appendix 7, each group of data has a principal
component (PC). PCA method used to deciding which of principal component will
be retained according to Kaiser’s criterion proposed by Coghlan (2014). The result
of variances each principal component is shown in Figure 13, while the value of
variances is shown in Table 3. Coghlan (2014) claimed that we should only retain
principal components for which the most obvious change in slope, the variance is
above one, or the total variance can explain at least minimum 80%.

Principal Component (PC)

Figure 13 Plot of variances each principal component (PC)

16
According to eq. 3 in Figure 13 shows that PC1 and PC2 have a significant
slope and they have the variance value is above 1 (21.584 and 2.572). Furthermore
the total variance can explain 89.46% of the variance. The proportion of variances
based on eq. 6 and variance are shown in Table 3 as following:
Table 3 The value of variances and proportion of variances
Proportion of
Variance
Variance
variances
PC15
0.018555334
PC1 21.583896143
0.7994
PC16
0.016110442
PC2
2.572216402
0.09527
PC3
0.849795768
0.03147
PC17
0.015785516
PC4
0.619347011
0.02294
PC18
0.013425849
PC5
0.438505317
0.01624
PC19
0.012373126
PC6
0.274578943
0.01017
PC20
0.010491372
PC7
0.181097206
0.00671
PC21
0.010073639
PC8
0.108886936
PC22
0.007663362
0.00403
PC9
0.064551947
0.00239
PC23
0.006569915
PC10 0.050391271
0.00187
PC24
0.004999698
PC11 0.040391268
0.0015
PC25
0.004143967
PC12 0.032474872
0.0012
PC26
0.003736582
PC13 0.031241860
0.00116
PC27
0.003388688
PC14 0.025307565
0.00094

Proportion of
variances
0.00069
0.0006
0.00058
0.0005
0.00046
0.00039
0.00037
0.00028
0.00024
0.00019
0.00015
0.00014
0.00013

Based on the deployment results of Kansei words between combination of
PC1 and PC2 obtained plot of Kansei word in Figure 14. Figure 14 shows that the
words: clear, simple form, general, standard, and normal, received large positive
loading value along the first principal component. On the other hand the words:
nice, eye catching, interesting, preferred, and practical to use received large
negative values. The first principal component was interpreted as the impression of
"Standard" and "Attractive".
Along the second principal component, the word: label colorful, bright,
illustrated, and can promote received large positive values. Large negative values
were assigned to traditional and classic along the same principal component. The
second principal component was interpreted as "Trendy" and "Classic". These
impressions will be the concepts targets in packaging design of Bogor pickle.

17

Trendy
Standard

Attractive

Classic

Figure 14 Plot of PC1 and PC2 weight distribution
17

18

Design Elements
Six design elements (E) looks at 2D appearance of form packaging from
twenty seven samples packaging (m) have been selected by discussion with five
expert panelist and study literature. Each of design element later will be identified
their type using morphological analysis technic using questionnaire as provided in
Appendix 8. As a result of the morphological analysis, Table 5 shows the six design
elements extracted from the twenty seven representative packaging samples,
together with their associated form types. Each design element has different form
types of its own, ranging from three to ten. For example, the ‘‘Top Shape (X1)’’
element has three form types, including ‘‘Concave curve (Cc, X1.1)’’, ‘‘Jaggy
concave curve (Jcc, X1.2)’’, and ‘‘Parallel line (Pl, X3.3)’’. The next step is
evaluating two pair of concepts based on twenty seven samples (m) of packaging
design by ten respondents who understand graphic design (Lin et al. 2007). This
evaluation using semantic differential questionnaire with 1-7 point scale, where 1
and 7 represented the standard look and the most attractive look, respectively. Table
4 shows the assessment result, as following:
Table 4 Numerical data source for the twenty seven representative packaging
samples
Samples Code X1 X2 X3 X4 X5 X6
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
AZ

1
2
3
3
3
3
2
2
3
3
3
3
3
3
1
3
1
1
1
3
3
1
3
3
3
3
3

6
3
1
1
8
1
2
7
1
2
2
4
1
1
9
8
9
4
5
7
9
5
1
4
10
8
10

2
1
2
1
2
2
1
2
2
2
2
1
2
2
2
2
2
3
3
2
1
1
2
2
2
2
2

1
1
1
2
3
1
4
4
4
2
1
1
1
1
3
3
3
2
6
5
3
2
1
7
7
7
7

2
2
1
2
2
2
3
3
3
1
1
1
2
1
1
1
1
2
2
2
1
2
2
1
3
2
2

3
3
3
3
3
3
3
1
2
2
1
2
1
2
3
1
3
3
2
1
1
1
1
3
1
1
1

Mean
2.7
3
2.4
4
2.2
3.1
2.4
3.2
5
5.2
4.2
5.1
4.8
4.6
5.5
4.1
4.4
4.9
5.5
4.9
4.3
5.1
5.5
4.6
5.1
4
3.1

Standard-Attractive
Clasic - Trendy
Min Max Stand. dev
Mean
1
6
1.531
3.6
1
6
1.877
3.1
1
5
1.226
2.9
2
5
1.226
3.7
1
4
0.999
2.7
2
5
0.988
3.6
1
7
1.759
2.8
1
6
1.593
3.4
3
7
1.191
5.1
3
7
1.152
5.2
2
7
1.234
4.7
3
7
1.188
5.7
3
7
1.399
5.6
2
7
1.338
5.4
3
7
1.235
5.5
2
7
1.309
4.6
2
6
1.276
4.5
1
7
1.804
4.9
3
7
1.226
5.8
2
7
1.651
4.9
2
6
1.146
4.6
2
7
1.333
5.4
4
7
1.137
6.2
3
6
1.099
4.3
3
7
1.522
4.9
1
7
1.997
4.5
1
6
1.717
2.9

19

Table 5 Morphological Analysis of the twenty seven representative samples of packaging
Design
Elements
Top Shape
(X1)

Type 1
Concave
curve
(Cc, X1.1)

Type 3

Type 4

(Jcc, X1.2)

Bottom
Shape (X3)

Arc
(A, X3.1)

Line
(Li, X3.2)

Curve
(C, X3.3)

Lid Shape
(X4)

Circular

Dome

Square

(Cr, X4.1)

(D, X4.2)

Small

Medium

Large

(S, X5.1)

(M, X5.2)

(L, X5.3)

Label Design Informative
(X6)
(Inf, X6.1)

Type 6

Type 7

Type 8

Type 9

Type 10

Prism

Standing
pouch
(Sp, X2.10)

(Pl, X1.3)

(T, X2.1)

Volume (X5)

Type 5

Jaggy convex Parallel line
curve

Jaggy tube Jaggy convex Jaggy bowl
tube
curve
(Jt, X2.2)
(Jct, X2.3) (Jbc, X2.4)

Body Shape
(X2)

Tube

Type 2

Circular with Eight square
handle
(Sq, X4.3) (Cwh, X4.4) (Es, X4.5)

Semiinformative Uninformative
(Sem, X6.2)

Short bowl
curve
(Sbc, X2.5)

(Uni, X6.3)

Spring cone Square tube
Square
eight
(Sc, X2.6) (Ste, X2.7) (Squ, X2.8)

Artistic

Sealer

(Ac, X4.6)

(Sl, X4.7)

(P, X2.9)

20

For each packaging samples in Table 4, the first column indicates the
packaging samples code and columns 2–7 shows the corresponding to type number
for its six design elements, respectively, as given in Table 5. For example,
packaging samples B code has the ‘‘Standard” image with an average “StandardAttractive” value of 3 as compared to compared to other packaging samples, and its
corresponding type numbers of the six design elements are X1=2, X2=3, X3=1,
X4=1, X5=2, and X6=3, respectively. Table 4 provides the numerical data source
for constructing the fuzzy rules to determine the value of the “Standard-Attractive”
image for a given packaging.
Design of Packaging
The last step is to examine the relationship between six design elements
(Independent variables) and two pair of design concepts (Dependent variable) using
QTT1 method according to eq. 7. In this study calculation QTT1 are supported by
R software, to generate R-square value, optimum category grade, and partial
correlation coefficient (PCC). From R-square value shows that "StandardAttractive" concept has larger value than "Trendy-Classic" concept; 0.9688>
0.6323. So "Standard-Attractive" concept will be used as a main image of
packaging design in develop of model using Type 2 Fuzzy Sets. The results of
QTT1 analysis is shown in Figure 15 and table 6 as following:
Information of notation :
X1.1 : Concave curve
X1.2 : Jaggy concave curve
X1.3 : Parallel line
X2.1 : Tube
X2.2 : Jaggy tube
X2.3 : Jaggy convex tube
X2.4 : Jaggy bowl curve
X2.5 : Short bowl curve
X2.6 : Spring cone
X2.7 : Square tube eight
X2.8 : Square
X2.9 : Prism
X2.10 : Standing pouch
X3.1 : Arc
X3.2 : Line

X3.3 : Curve
X4.1 : Circular
X4.2 : Dome
X4.3 : Square
X4.4 : Circular with
handle
X4.5 : Eight square
X4.6 : Artistic
X4.7 : Sealer
X5.1 : Small
X5.2 : Medium
X5.3 : Large
X6.1 : Inform