Desain Afektif Untuk Kemasan Asinan Bogor.

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AN AFFECTIVE DESIGN FOR BOGOR PICKLE

PACKAGING

NOVI PURNAMA SARI

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

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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 All of incorporated originated references from other published as well as unpublished papers are stated clearly in the text as well as in the references.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

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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

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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 Standard-Attractive 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,

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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 K-means 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

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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

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© Copyright 2015 by IPB

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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.

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Thesis

as partial fulfillment of the requirements for the degree of Master of Science

in the Agroindustrial Technology Study Program

AN AFFECTIVE DESIGN FOR BOGOR PICKLE

PACKAGING

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

2015

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Dr Eng Taufik ｄｪｾｴｮ｡Ｌ＠ STP MSi Chaim1an

Head of

Agroindustrial Technology Study Program

Approved by Supervisor

Dr Mirwan U sh

Acknowledged by

Prof Dr Ir Machfud, MS Dr Ir Dahrul Syah, MSc Agr

Examination date: Passed date:

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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

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TABLE OF CONTENTS viii

LIST OF TABLES ix

LIST OF FIGURES ix

LIST OF APPENDIX ix

1 INTRODUCTION 1

Background of Research 1

Problem Statement of Research 3

Objective of Research 4

Benefit of Research 4

Boundaries of Research 4

2 METHODOLOGY 4

Research Framework 4

Determination Domain of Customer 6

Selection Design Concept 6

Determination Design Elements 8

Synthesis Design of Packaging Using Quantification Theory Type 1 8 Building the Model Using Interval Type 2 Fuzzy Sets 8

4 RESULT AND DISCUSSION 10

Domain of Customer 10

Aspect of Customer Needs 11

Design Concepts 13

Design Elements 18

Design of Packaging 20

The Model Evaluation 24

5 CONCLUSION AND RECOMMENDATION 29

Conclusion 29

Recommendation 30

REFERENCES 30

APPENDIXES 32

GLOSSARY 50

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LIST OF TABLES

1 Identification of customer needs for pickles packing 12

2 Data set of Kansei word 15

3 The value of variances and proportion of variances 16 4 Numerical data source for the twenty seven representative packaging

samples 18

5 Morphological Analysis of the twenty seven representative samples of

packaging 19

6 Value of category grade and partial correlation coefficient each design

element 21

7 Input and output in IT2FS model 24

8 Triangular fuzzy numbers for the top shape (X1) form element 26 9 Triangular fuzzy numbers for the packaging image (Y) 26 10 Membership function formulation for top shape 26 11 Fuzzy rules for determining the S–A value of pickle packaging 27

12 The RMSE results of the IT2FS 29

LIST OF FIGURES

1 Level of customer complaints about Bogor pickle (Kartasasmita 2014) 1

2 A hierarchy of consumer needs (Jordan 200) 2

3 A proposed Model on Kansei Engineering (Schütte 2006) 3 4 An affective design framework of Bogor pickle based on KE 5 5 Example of semantic differential questionnaire with 7-point scale 6 6 Membership function for fuzzy type 2 (Mendel et al. 2006) 9 7 Stages of development IT2FS (Mendel et al. 2006) 9 8 The good cluster solution without average negative silhouette score 10 9 Plot results of cluster market segmentation 11

10 The four pleasures (Jordan 2000) 11

11 Tools for support stimulate respondents 13

12 Representation of twenty seven packaging samples 14 13 Plot of variances each principal component (PC) 15

14 Plot of PC1 and PC2 weight distribution 17

15 QTT1 bar graphic score category of standard-attractive concept 20

16 Representation 2D and 3D image of QTT1 22

17 Representation of informative design label 23

18 The triangular MF of input (X1 to X6) and output (Y) 25 19 User profile interface of model evaluation program 28 20 Operation interface of model evaluation program 28

LIST OF APPENDIX

1 Profile of respondent 32

2 Data identification of market segmentation 34

3 Market segmentation questionnaire 35

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5 Packaging samples 38 6 Identification of packaging design elementsquestionnaire 40

7 Evaluation Kansei wordquestionnaire 42

8 Data of Kansei word evaluation 44

9 Representation image of label on design packaging using QTT1 45 10 Member function specialty food gift product 46

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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’

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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 non-verbal 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

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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?

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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.

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Start

Identifying market segmentation [Preface Ques. 1]

Cluster analyszing by

Pillar K-means Algorithm

Market Segmentation

Collecting the Kansei word [Impression Ques.] Evaluating the Kansei word

[Semantic-Differential Ques.]

Design concept Extracting Kansei

word by PCA

Evaluating design concept [SD-concept Ques.]

Collecting the pckaging samples [Market survey]

Identifying of design elements in the samples by Morphology Analysis

[Identify-elements Ques.]

Tabulation of data Tabulating samples based

on design elements

Correlation analyzing of design element and concept design by QTT1

Design of packaging

Determining Domain of Customer

Sellecting Design Concept

Determining Design Elements

Synthesis Design of Packaging

Samples

Note :

= Flow of information from customer

= Flow of information from expert panelists

= Flow of information from result processed in researchers

Determining input and output

Model evaluation

Building the Model

Determining the membership functions of input and output

Identifying aspect of Customer needs [Preface Ques. 2]

Customer needs

Building the fuzzy rules by IT2FS

Evaluating performance of model

Design elements

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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).

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)

-3 -2 -1 0 1 2 3

Classic Trendy

Uncomfortable Comfortable

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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 1 n i k q q n 





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2. Subtract Z matrix in eq. 2

Z



q₁q q, ₂q,...q_nq



(2)

3. Determine variance in eq. 3 and covariance in eq. 4

2 1 1 ( ) ( ) m j j

Var x q q

m 





 (3)

* ( 1)

T k k

C Z Z



 (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

1 1 g j j g m j j      



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

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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):

1 1

n E C

st stm m

s t

y  X 

 





 (7) Where

y 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):

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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:

 

 





' ' '

( ) _x _X _x , : _x 0,1

FOU A  _{ } J  x u uJ  (8)

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:

1 1

: A ... A , , 1, 2,...,

p p p p

i l

R IF x is and and x is THEN b is B p P (9)

where �̃₁� is antecedent, Bp is consequent on fuzzy rules, x1....xi is input to

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:

 

1 ... 1 , 1 ... 1

p p

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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:

_� =∑��=1��

∑�_�=1�_�� (11)

and

_� = ∑��=1��

∑� �_��

�=1 (12) 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:

 

l r

b b

b x   (13)

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.

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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

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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

Functionality Packaging as container products

need Packaging as protective

Usability

need

Safe packaging used are made from PP plastic, food grade, heat resistant and not susceptible migration

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

Pleasurable need

Physical The form of packaging comfortable to held Large packaging added by handle

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

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Symbol of product brand attractive and easy to remember

Social

The packaging gives a sense of pride to someone when using

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

Ideological

Packaging materials using food grade materials 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

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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 7-point 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).

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Table 2 Data set of Kansei word

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%.

Figure 13 Plot of variances each principal component (PC)

No Kansei word No Kansei word

1 Trendy - Classic 21 Khas - Common

2 Cozy - Uncomfortable 22 Innovative - Conservative

3 Plus illustrations - Without illustrations 23 Aesthetics - Natural

4 Safe - Dangerous 24 Artistik - Not artistic

5 Modern - Traditional 25 Protection - Not protection

6 Unique - General 26 Informative - Not informative

7 Simple typography - Complex typography 27 Communicative design - Uncommunicative design

8 Nice - Poor 28 Characteristic - Not characterized

9 Atrractive - Not Atrractive 29 Eyechatching - Bright color

10 Bright - Pasty 30 Clearly label - Faded label

11 Colorful labels- Plain labels 31 Identity - Without identity

12 Hardy - Brittle 32 Describe the product - Not describe the product 13 Practical to use- Difficult to use 33 Liked - Disliked

14 Complex -Simple 34 Pleasure - Not pleasure

15 Exotic - No exotic 35 Labels can promote - Labels can not promote 16 Functional - Not functional 36 Matching - Not applicable

17 Flexible - Rigid 37 Different - Not different

18 Easy - Difficult 38 Luxurious - Standard

19 Transparent - Opaque 39 Ergonomic - Not ergonomic

20 Large - Small 40 Glory design - Disappointing design

Principal Component (PC)

Var

ian

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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 Variance Proportion of

variances Variance

Proportion of variances

PC1 21.583896143 0.7994 PC15 0.018555334 0.00069

PC2 2.572216402 0.09527 PC16 0.016110442 0.0006

PC3 0.849795768 0.03147 PC17 0.015785516 0.00058 PC4 0.619347011 0.02294 PC18 0.013425849 0.0005 PC5 0.438505317 0.01624 PC19 0.012373126 0.00046 PC6 0.274578943 0.01017 PC20 0.010491372 0.00039 PC7 0.181097206 0.00671 PC21 0.010073639 0.00037 PC8 0.108886936 0.00403 PC22 0.007663362 0.00028 PC9 0.064551947 0.00239 PC23 0.006569915 0.00024 PC10 0.050391271 0.00187 PC24 0.004999698 0.00019 PC11 0.040391268 0.0015 PC25 0.004143967 0.00015 PC12 0.032474872 0.0012 PC26 0.003736582 0.00014 PC13 0.031241860 0.00116 PC27 0.003388688 0.00013 PC14 0.025307565 0.00094

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.

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Figure 14 Plot of PC1 and PC2 weight distribution

Attractive

Classic Trendy

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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 Clasic - Trendy

Mean Min Max Stand. dev Mean

A 1 6 2 1 2 3 2.7 1 6 1.531 3.6

B 2 3 1 1 2 3 3 1 6 1.877 3.1

C 3 1 2 1 1 3 2.4 1 5 1.226 2.9

D 3 1 1 2 2 3 4 2 5 1.226 3.7

E 3 8 2 3 2 3 2.2 1 4 0.999 2.7

F 3 1 2 1 2 3 3.1 2 5 0.988 3.6

G 2 2 1 4 3 3 2.4 1 7 1.759 2.8

H 2 7 2 4 3 1 3.2 1 6 1.593 3.4

I 3 1 2 4 3 2 5 3 7 1.191 5.1

J 3 2 2 2 1 2 5.2 3 7 1.152 5.2

K 3 2 2 1 1 1 4.2 2 7 1.234 4.7

L 3 4 1 1 1 2 5.1 3 7 1.188 5.7

M 3 1 2 1 2 1 4.8 3 7 1.399 5.6

N 3 1 2 1 1 2 4.6 2 7 1.338 5.4

O 1 9 2 3 1 3 5.5 3 7 1.235 5.5

P 3 8 2 3 1 1 4.1 2 7 1.309 4.6

Q 1 9 2 3 1 3 4.4 2 6 1.276 4.5

R 1 4 3 2 2 3 4.9 1 7 1.804 4.9

S 1 5 3 6 2 2 5.5 3 7 1.226 5.8

T 3 7 2 5 2 1 4.9 2 7 1.651 4.9

U 3 9 1 3 1 1 4.3 2 6 1.146 4.6

V 1 5 1 2 2 1 5.1 2 7 1.333 5.4

W 3 1 2 1 2 1 5.5 4 7 1.137 6.2

X 3 4 2 7 1 3 4.6 3 6 1.099 4.3

Y 3 10 2 7 3 1 5.1 3 7 1.522 4.9

Z 3 8 2 7 2 1 4 1 7 1.997 4.5

AZ 3 10 2 7 2 1 3.1 1 6 1.717 2.9

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Table 5 Morphological Analysis of the twenty seven representative samples of packaging Design

Elements

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

Concave curve

Jaggy convex curve

Parallel line

(Cc, X1.1) (Jcc, X1.2) (Pl, X1.3)

Tube Jaggy tube Jaggy convex

tube

Jaggy bowl curve

Short bowl curve

Spring cone Square tube

eight

Square Prism Standing

pouch (T, X2.1) (Jt, X2.2) (Jct, X2.3) (Jbc, X2.4) (Sbc, X2.5) (Sc, X2.6) (Ste, X2.7) (Squ, X2.8) (P, X2.9) (Sp, X2.10)

Arc Line Curve

(A, X3.1) (Li, X3.2) (C, X3.3)

Circular Dome Square Circular with

handle

Eight square Artistic Sealer

(Cr, X4.1) (D, X4.2) (Sq, X4.3) (Cwh, X4.4) (Es, X4.5) (Ac, X4.6) (Sl, X4.7)

Small Medium Large

(S, X5.1) (M, X5.2) (L, X5.3)

Informative

Semi-informative UnSemi-informative

(Inf, X6.1) (Sem, X6.2) (Uni, X6.3) Top Shape

(X1)

Label Design

(X6)

Volume (X5)

Body Shape

(X2)

Bottom Shape (X3)

Lid Shape

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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 : Informative X6.2 : Semi-informative X6.3 : Un-informative

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 “Standard -Attractive” 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 "Standard-Attractive" 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:

Figure 15 QTT1 bar graphic score category of standard-attractive concept The partial correlation coefficients (PCC) indicates the relationship between the six design elements and a pair of design concepts. The highest variable of PCC on "Standard-Attractive" concept was "Design Label" (X6 = 0.97872), so this variable was great influence on customer perception, as given in Table 6.

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Table 6 Value of category grade and partial correlation coefficient each design elements

Design

Elements Type

Category grade Partial correlation

coefficient

(PCC) (From type grade)

Standard Attractive

X1 _Top

Shape

X1.1 Concave curve 2.18556 0.96643

X1.2 Jaggy convex curve -0.54444

X1.3 Parallel line -0.63778

X2 Body Shape

X2.1 Tube 1.3942 0.97146

X2.2 Jaggy tube 0.88753

X2.3 Jaggy convex tube 1.40753

X2.4 Jaggy bowl curve 1.9542

X2.5 Short bowl curve -2.56247

X2.6 Spring cone -1.68247

X2.7 Square tube eight -0.6058

X2.8 Square -1.35914

X2.9 Prism -1.0258

X2.10 Standing pouch -2.25914

X3 Bottom

Shape X3.1 Arc 0.14173 0.91420

X3.2 Line 0.20173

X3.3 Circular -2.3416

X4 Lid Shape

X4.1 Circular -0.75222 0.96197

X4.2 Dome 0.35444

X4.3 Square 1.32111

X4.4 Circular with

handle -2.93889

X4.5 Eight square 0.96111

X4.6 Artistic 3.34444

X4.7 Sealer 0.81444

X5 Volume X5.1 Small -0.61432 0.93468

X5.2 Medium -0.13432

X5.3 Large 1.97235

X6 Design

Label X6.1 Informative 0.93358 0.97872

X6.2 Semi-informative 0.82691

X6.3 Un-informative -1.29975

Constants = 4.18148

R = 0.7295

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X1.1: Concave curve

X2.4: Jaggy bowl curve

X3.2: Line X4.6: Artistic

X5.3: Large (Volume: 1.000 L) Specifications dimensions:

⏀Top diameter: 18 cm

⏀Bottom diameter: 8 cm Foot height: 2 cm

Body height: 10 cm Lid height: 4 cm QTT1 bar graphic in Figure 15 shows the optimum of each design element of “standard-attractive” concept, while representation packaging image of each design element by QTT1 is shown in Figure 16, for more detail as provided in Appendix 9.

a) Front view

b) Right oblique view

Figure 16 Representation 2D and 3D image of QTT1; a) front view, b) right oblique view

Figure 15 and Figure 16 shows that the result of QTT1 analysis was gotten the design concept of "Attractive" produce the design elements such as: Top Shape is Concave curve (X1.1), Body Shape is Jaggy bowl curve (X2.4), Bottom Shape:

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Brand

Volume and

Expired Information

Product Ilustration Halal Information Brand Icon

Invitation visit to Bogor

Ice ilustration to cool image

Nutritions Information and

Composition Information of Company

Tourism Information

Information for Storage

Presentation Suggestion Line (X3.2), Lid Shape is Artistic (X4.6), Volume is Large (X5.3), while Design Label is Informative (X6.1) is shown in Figure 17. They are corresponding by labeling regulations based on BPOM Indonesia (BPOM 2004).

a) Front view

b) Back view

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The specification of the design is to use PP material because it is one type of plastic that is resistant and better than other, as given in Table 1. The color of plastic to lid is red and a body parts is transparent. The function of transparent in body parts is to provide the level of consumer confidence in product contents and increase consumer appeal when they look products. The advantage of this design is can increase of the local wisdom. According to observation about habit of consumer when consume pickle is generate first impression for product as togetherness. They usually consume pickle together with family or friends, that is supported by the large size and bowl shape of packaging design. Whereas to lower potential of leak packaging by added sealer before the product is closed. Part of the empty space inside the lid and sealer can be used to place additional items of products such as beans, a chili sauce, and a spoon. In Figure 17 shows informative design label. One example of attractive information is tourist information. It is can explain to consumer about tourist attraction in Bogor. The result of packaging design by QTT1 can fulfill all aspect of pleasurable need in Table 1.

The Model Evaluation

Building a fuzzy logic model involves the definition of the input and output linguistic variables and the building of the fuzzy rules. Step by step in build of IT2FS model are:

a. Determining the input and output

In line with the outcome of our previous study, In line with the outcome of our previous study, the fuzzy rules in IT2FS model were constructed by using the six design elements of pickle packaging (as the input). While the output linguistic variable (B) is the “Standard-Attractive” image, whose value is the average “Standard-Attractive” value assessed by the ten respondents who understand graphic design, as given in Table 5. Table 7 shows input and output in IT2FS model.

Table 7 Input and output in IT2FS model

b. Determining the membership functions of input and output

A triangular or trapezoid form of the membership function (MF) is used most often for representing type 2 fuzzy numbers (Mendel et al. 2006). The equation 14 shows the membership function A (x) of a triangular fuzzy number represented by a triple (a~,b~,c~), where a~, b~, and c~ are real numbers with a~≤

~ b≤

~ c:

Design Elements

1 2 3 4 5 6 7 8 9 10

INPUT Top Shape (X1) Cc Jcc Pl

Body Shape (X2) T Jt Jct Jbc Sbc Sc Ste Squ P Sp

Bottom Shape (X3) A Li C

Lid Shape (X4) Cr D Sq Cwh Es Ac Sl

Volume (X5) S M L

Label Design (X6) Inf Sem Uni

OUTPUT Packaging Image

"Standard-Attractive" Es Vs S N A Va Ea

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~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 , , ( ) , 0 x a x a

a x b

b a A x

x c

b x c

b c x c   _     _{ }       _{ }        (14)

The triangular fuzzy number (a~,b~,c~) can be used to give the approximate value range of a linguistic term. b~ is the most possible value of the term, and a~ and c~ are the lower and upper bounds, respectively, used to reflect the fuzziness of the term. In this paper, the triangular membership functions is used to represent the values of various form types (i.e. the linguistic terms) of the six design elements used in the fuzzy rules of the IT2FS model, is shown in Figure 18.

1 2 3 4 5 6 7 8 9 10 1 T Jt Jct Jbc Sbc Sc Ste Squ P Sp

1 2 3 4 5 6 7 1

Cr D Sq Ch Es Ac Sl

x 1A Li C

1 2 3 _{1 2 3}

1S L M

1 2 3 1 Inf Sem Uni

Packaging Image (Y)

1 2 3 4 5 6 7 1

Es Vs S N A Va Ea

Lid Shape (X4) Volume (X5)

Design Label (X6) Bottom Shape (X3)

Boddy Shape (X2)

( ) A x



Top Shape (X1)

1 2 3 1 x ( ) A x  ( ) A x  ( ) A x

 A x( )

( ) A x

 A x( )

Cc Jcc Pl

Figure 18 The triangular MF of input (X1 to X6) and output (Y)

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Each linguistic variable in Figure 18 is defined according to the number of the form types of its underlying form element, given in Table 5. For example, ‘‘Top Shape (X1)’’ has three form types (Cc, Jcc, Pl) characterized by three triangular fuzzy numbers are used, is shown in Table 8. While “Packaging Image” or “Standard-Attractive” (S-A) image has seven possible S–A values are fuzzified with seven triangular fuzzy numbers to represent the seven value states (i.e. the seven linguistic terms) of the S–A image (as the output linguistic variable), respectively, are shown in Table 9 and Figure 18.

Table 8 Triangular fuzzy numbers for the top shape (X1) form element

Linguistic term Upper MF (UMF) Lower MF (LMF)

Concave curve (Cc) (1; 1; 2) (1; 1; 1.5)

Jaggy convex curve (Jcc) (1; 2; 3) (1.5; 2; 2.5) Parallel line (Pl) (2; 3; 3) (2.5; 3; 3) Table 9 Triangular fuzzy numbers for the packaging image (B)

Linguistic term Upper MF (UMF) Lower MF (LMF)

Extreme standard (Es) (1; 1; 2) (1; 1; 1.5) Very standard (Vs) (1; 2; 3) (1.5; 2; 2.5)

Standard (S) (2; 3; 4) (2.5; 3; 3.5)

Neutral (N) (3; 4; 5) (3.5; 4; 4.5)

Attractive (A) (4; 5; 6) (4.5; 5 ; 5.5)

Very attractive (Va) (5; 6; 7) (5.5; 6 ; 6.5) Extreme attractive (Ea) (6; 7; 7) (6.5; 7 ; 7)

The example equation membership function UMF and LMF of “Top shape” is shown in Table 10, and to all equation membership function each input and output as provided in Appendix 10.

Table 10 Membership function formulation for top shape

Upper MF (UMF) Lower MF (LMF)

 

' ' '

0 , 2

2 , 1 2

1 , 1

Cc x x x

x     _     _ 

 

' ' ' ' ' ' '

0 , 1 3

1 , 1 2

3 , 2 3

x or x

Jcc x x x

x x      _        

 

' ' ' ' '

0 , 2

2 , 2 3

1 , 3

Pl x x x

x     _     _ 

 

' ' ' ' '

0 , 1.5

1.5

, 1 1.5

0.5

1 , 1

x x

Cc x x

x       _     

 

' ' ' ' '

0 , 1.5 2.5

1.5

, 1.5 2

0.5 2.5

, 2 2.5

0.5

x or x

Jcc x x

x x         _      _{ } 

 

' ' ' ' '

0 , 2.5

2.5

, 2.5 3

0.5

1 , 3

x x

Pl x x

x       _     

(49)

c. Building the fuzzy rules

Rules are used in the models IT2FS will be used in the process inference. This rules are mapping linguistic value of each type for each design element as input to the Standard-Attractive linguistic value as output. A new experimental process is conducted to objectively generate a set of fuzzy rules with design elements, based on the respondent assessments of the standard–attractive image on twenty seven representative packaging samples using semantic differential questionnaire with 1-7 point scale, moreover the rules are also generated by discussion with expert panelist. To reflect the product form design process involving multiple form elements, we use the fuzzy rules with multiple conditions (antecedents), as follows in eq. 9. Table 11 shows the results of some the rules that are used in IT2FS model. For example the meaning of first rule in Table 11 as following:

“IF Top Shape is Concave curve AND Body Shape is Jaggy bowl curve AND

Bottom Shape is Line AND Lid Shape is Artistic AND Volume is Large

AND Design Label is Informative THEN Packaging Image is Attractive” Table 11 Fuzzy rules for determining the S–A value of pickle packaging

THEN (Consequent)

X1 X2 X3 X4 X5 X6 S–C (B)

1 concave curve jaggy bowl curve line artistic large informative attractive

2 concave curve prism line square small informative standard

3 concave curve prism line square small semi informative very standard

4 _{concave curve} _{spring cone} line _artistic _medium informative _{very attractive}

5 _{concave curve short bowl curve} _arc _{circular handle medium} _{un informative} _{extreme standard}

6 _{concave curve} _{jaggy tube} _curve _dome _medium _informative _{very attractive}

7 _{concave curve} _{jaggy tube} _{curve circular handle} _large _{semi informative} _attractive

8 _{jaggy convex} _{jaggy tube} _{line circular handle} _large _{un informative} neutral

9 _{jaggy convex jaggy convex tube arc} _circular medium _{un informative} _standard

10 jaggy convex spring cone curve circular handle large semi informative neutral

... ... ... ... ... ... ... ...

410 jaggy convex square tube line square small informative attractive

411 jaggy convex square tube line circular small semi informative neutral

412 jaggy convex jaggy tube arc dome medium informative very attractive

413 jaggy convex spring cone line artistic small informative very attractive

414 parrallel line jaggy bowl curve line circular handle large un informative very standard

415 parrallel line jaggy bowl curve arc artistic medium informative attractive

416 parrallel line tube line circular small informative standard

417 parrallel line jaggy bowlcurve curve artistic medium semi informative standard

418 parrallel line square line square medium un informative extreme standard

419 _{parrallel line} _{standing pouch} line _sealer _large _{un informative} _standard

420 _{parrallel line} _{square tube} _line _square _large _{un informative} _{extreme standard}

(50)

Furthermore, the membership functions and rules are used in the calculation of firing interval in accordance with the eq. 10, the reduction process on the type of eq. 11 and eq. 12, and the last process of defuzzified in eq. 13. The program was prepared to easy operate (user friendly), and an interesting display (user interface). Figure 19 and Figure 20 are interface in an application that has been developed called KAPIPAP 1.0 program.

Figure 19 User profile interface of model evaluation program

(1)

 

' '

' ' '

' '

0 , 4 6

4 , 4 5

6 , 5 6

x or x

Sbc x x x

' ' '

0 , 10

1 , 10

x Sp x x        _ 

 

' ' ' ' '

0 , 4.5 5.5

4.5

, 4.5 5

0.5 5.5

, 5 5.5

' ' '

0 , 10

1 , 10

x Sp x x        _  Bottom Shape

(X3)

 

' ' '

0 , 2

2 , 1 2

1 , 1

A x x x

x     _     _ 

 

' ' ' ' ' ' '

0 , 1 3

1 , 1 2

3 , 2 3

x or x

Li x x x

x x      _        

 

' ' ' ' '

0 , 2

2 , 2 3

1 , 3

C x x x

x     _     _ 

 

' ' ' ' '

0 , 1.5

1.5

, 1 1.5

0.5

1 , 1

x x

A x x

x       _     

 

' ' ' ' '

0 , 1.5 2.5

1.5

, 1.5 2

0.5 2.5

, 2 2.5

0.5

x or x

Li x x

x x         _      _{ } 

 

' ' ' ' '

0 , 2.5

2.5

, 2.5 3

0.5

1 , 3

x x

C x x

x       _     

(2)

48 Lid Shape

(X4)

' ' ' ' '

0 , 5

5 , 5 6

1 , 6

Ac x x x

x     _     _ 

 

' ' '

0 , 7

1 , 7

x Sl x x        _ 

' ' ' ' '

0 , 5.5

5.5

, 5.5 6

0.5

1 , 6

x x

Ac x x

x       _     

 

' ' '

0 , 7

1 , 7

x Sl x x        _ 

(3)

Volume

(X5)

 

' ' '

0 , 2

2 , 1 2

1 , 1

S x x x

x     _     _ 

 

' ' ' ' ' ' '

0 , 1 3

1 , 1 2

3 , 2 3

x or x

M x x x

x x      _        

 

' ' ' ' '

0 , 2

2 , 2 3

1 , 3

L x x x

x     _     _ 

' ' '

0 , 2

2 , 1 2

1 , 1

Inf x x x

x     _     _ 

 

' ' ' ' ' ' '

0 , 1 3

1 , 1 2

3 , 2 3

x or x

Sem x x x

x x      _        

 

' ' ' ' '

0 , 2

2 , 2 3

1 , 3

Uni x x x

x     _     _ 

Clustering

One of the unsupervised learning techniques where we do not need to practice these methods, or in other words, there is no learning phase

Design

One part of a system for devise a product Functionality

One aspect of customer needs that focus on the function of a product Footprint of Uncertainty (FOU)

A limited area that contains the primary uncertainty membership degree of membership function type 2

Informative

Something that is giving information or explicative Interval Type 2 Fuzzy Sets (IT2FS)

Development of methods of fuzzy type 1 which has a better ability in analyzing a matter of uncertainty

Kansei

A Japanese word that means emotion, feeling, affection. Kansei/Affective Engineering

A method to get the user requirements and user preferences based on his sense and cognition

Kansei word

Generally used an adjective, although it can also noun Kapipap

One of the programs developed using java language program with a system based on IT2FS to evaluate the design of pickles packaging

Linguistic

Relating to language, depending on the angle of view, and approach a researcher, linguistic often classified into cognitive science, psychology, and anthropology. Low Membership Function (LMF)

The lower limit of the membership function to generate FOU area Market Segmentation

A marketing strategy which involves dividing a broad target market into subsets of customer, businesses, or countries who have, or are perceived to have, common

(5)

needs, interests, and priorities, and then designing and implementing strategies to target them.

Membership Function

A membership function on Fuzzy logic (with a range covering the interval (0,1)) operating on the domain of all possible values.

Morphological Analysis

A method to analyze the physical similarity of the sample, so design elements of the packaging can determined

Partial Correlation Coefficients (PCC)

Indicators to determine the variable or design element most big impact on customer perception

Pillar K-means Algorithm

The development of K-means clustering method is more objective and has a good capability

Pleasurable

One aspect of customer needs that very important because focus on the pleasure of customer

Principal Component Analysis (PCA)

A multivariate statistical technique that allows finding correlations among variables represented by a set of observations stored in a matrix.

Polypropylene (PP)

A thermoplastic polymer used in a wide variety of applications including packaging and labeling, textiles. One variety of plastics that good resistance to heat and good to food packaging

Quantification Theory Type 1 (QTT1)

One method to analysis correlation between design element and design concept Root of Mean Square Errors (RMSE)

One method to evaluate performance of model Semantic Differential

A technique for attitude measurement, scaling people on their responses to adjectives in respect to a concept.

Synthesis

One of the stages in the process of Kansei engineering method that serves as a merger

Upper Membership Function (UMF)

(6)

52 Usability

The extent to which a product or system effectively and efficiently satisfy the needs and specifications of users.

Triangular

One form of membership function in the fuzzy model

BIOGRAPHY

I am Novi Purnama Sari who was born in Baradatu on the November 21th 1989, as the youngest child of Mr. Johari and Mrs. Istikana. I graduated from SMAN 10 Yogyakarta in the 2007 and continued as an undergraduate student in the Agroindustrial Technology, Gadjah Mada University, since 2007 to 2011. After graduation I have work experience in PT Mayora Indah Indonesia (2012) and PT Great Giant Pineapple (2013). I entered Graduate School of Bogor Agricultural University, majoring Agroindustrial technology (Master Program), in September, 2013 with the sponsorship by Ministry of Education that have provided me with scholarship enable for this master courage completion. I am very interested to research in the field of product development, especially related to Kansei Engineering. I have been conducted research on Kansei Engineering since 2010 until today. This research has been presented in international conference of the first symposium global halal with the title “Traditional Pickle Fruits Product Design

Evaluation Based on Halal Food and Interval Fuzzy Set”, and has been reviewed in

the agricultural industry technology journal (IPB) with the title “Desain Kemasan Produk Asinan Menggunakan Teori Kuantifikasi Tipe 1 Berbasis Kansei Engineering”.