RASHID -ITC Proceeding Book

Arini Hidayati Jamil : Sisal Fiber Of Agave H11648 as A Potential Raw Material For Eco-Friendly Textile

6. FAO, 2019. Future Fibers : Sisal [WWW Document]. URL
http://www.fao.org/economic/futurefibres/fibres/sisal/en/ (accessed 6.26.19).

7. Gintare, 2018. Sisal / Agave sisalana - Very Durable and Highly Absorbent Textile Fabric [WWW
Document]. URL https://www.amberoot.com/sisal-agave-sisalana-very-durable-and-highly-absorbent-
textile-fabric (accessed 5.21.19).

8. Huang, X., Xie, L., Jr, T.G., Xi, J., Yi, K., 2019. Transcriptome Dataset of Leaf Tissue in Agave H11648. Data
4, 3–7.

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https://doi.org/10.3390/fib3010064

10. Jamil, A.H., Tjahjono, H.J., Parnidi, P., Marjani, M., 2018. Potensi Penggunaan Dua Spesies Agave untuk
Pembuatan Pulp dan Kertas. J. Selulosa 8, 43–50. https://doi.org/10.25269/jsel.v1i01.229

11. Li, Y., Sreekala, M.S., Jacob, M., 2008. Textile Composites Based on Natural Fibers, in: Thomas, S., Pothan,
L.A. (Eds.), Natural Fibre Reinforced Polymer Composites from Macro to Nanoscale. Old City Publishing,
Inc., Philadelphia, pp. 202–227.

12. Magaton, A.S., Mascena da Cunha, C., Peixoto, M. de A., Conceicao, S. dos S., 2015. Hemicelluloses
extraction from Agave sisalana and Hybrid 11648, in: &th International Colloqium on Eucalyptus Pulp.
Vitoria, Espirito Santo, Brazil.

13. Manyam, S., Alapati, P., 2018. Eco-Friendly Sisal Union Fabric-Suitability Assesment. Int. J. Educ. Sci. Res.
8, 77–84.

14. Methacanon, P., Weerawatsophon, U., Sumransin, N., Prahsarn, C., Bergado, D.T., 2010. Properties and
potential application of the selected natural fibers as limited life geotextiles. Carbohydr. Polym. 82, 1090–
1096. https://doi.org/10.1016/J.CARBPOL.2010.06.036

15. Santoso, B., 2009. Peluang pengembangan agave sebagai sumber serat alam. Perspektif 8, 84–95.
16. Setyo-Budi, U., Marjani, Purwati, R.D., Murianingrum, M., 2017. Pelepasan Klon H 11648 sebagai Varietas

Unggul Tanaman Sisal.
17. Shukla, A., Basak, S., Ali, S.W., Chattopadhyay, R., 2017. Development of fire retardant sisal yarn.

Cellulose 24, 423–434. https://doi.org/10.1007/s10570-016-1115-7
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http://www.wildfibres.co.uk/html/sisal_fibre.html
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DOI : 10.5281/zenodo.3470929

Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Designing Batik and Artificial Batik Differentiator
Applications Using Tensorflow

Isnaini 1, Dwi Wiji Lestari 1, Paras Trapsiladi 1, Zohanto Widyatmoko 1, Euis Laela 1, Irfa’ina Rohana
Salma 1, Masiswo 1, Joni Setiawan 1, Vivin Atika 1, Yudi Satria 1, Agus Haerudin 1, Guring Briegel

Mandegani 1, Tri Kusuma Arta 1, Novita Ekarini 1, Tika Sulistyaningsih 1,and Dana Kurnia Syabana

1

1 Balai Besar Kerajinan dan Batik; [email protected]

* Correspondence: [email protected]; Tel.: +62-87838241410

Abstract: Batik is the pride and masterpiece heritage of Indonesia. Batik has awarded as cultural
heritage from UNESCO on October 2nd, 2009 and it is significantly affected to batik industry
afterward. UNESCO has recognized batik as a traditional textile produced by using a technique of
wax-resist dyeing applied to the fabric. Many products are produced to resemble batik, however along
with technological innovations the products do not use hot wax. It will be difficult for the public to
distinguish between the real batik and artificial batik available on the market. In order to determine
whether the item is a real batik product or a falsified product, a tool is required. Center for Crafts and
Batik has conducted research on making the "Batik Analyzer" software. The software's design is made
using "deep learning" technology. The software uses TensorFlow. TensorFlow is a computational
framework for making "machine learning" models. Machines are trained to be able to learn the
difference in products by providing learning tools in the form of authentic batik which are: stamp
batik and hand-written batik; artificial product which are: batik print imitation, cold wax batik print
imitation, and burn-out print batik imitation products. The application that was created later was
designed to run on an Android-based smartphone or tablet. Applications are then trained to recognize
the product. The used data set is taken at The Center for Crafts and Batik for each type of fabrics to be
distinguished. The results of the exercise are tested to be able to recognize the product. From the
results of the training, it can be found that the software can distinguish products with 70 percent
accuracy.

Keywords: batik; batik analyzer; stamped batik; hand written batik; artificial batik; TensorFlow

ISBN : 978-623-91916-0-3

1. Introduction

The process of making batik goes through several stages. These steps are done manually. In
written batik, starting with the making pattern on the fabric using a pencil. For one piece of written

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batik, complex pattern work can take days. Pattern work is continued with the wax sticking work on
the edge of the motif or transferring motifs which in batik terms are called klowong. Sticking the wax
to the klowong section is the longest job. The motif was first completed with the first coloring work. If
you want there are several colors in a piece of fabric, then the first color you want is received by the
process of closing the parts with melted wax (called : nembok). The nembok work continued to the
coloring. To eliminate the wax, the wax release process is done by boiling it. The wax that sticks to the
fabric will come loose and float on the surface of the water. The work of making batik can take a month
or more. Batik stamps are made faster than batik. By using tools called canting cap, nglowong work
can be accelerated to just a few hours.

Along with the development and demands of the economy, many artificial batik has been
produced. Its manufacture does not involve the process of sticking hot wax for color resist. Artificial
batik can be made faster and can be done in mass scale. Stated that the popularity of batik affected the
use of textile technology which was able to accelerate the production of batik-patterned fabrics. This
technology is capable to produce tens to hundreds of pieces of batik cloth in a day. However, the
acceleration of production capability eliminated the cultural essence of the batik, because batik which
is recognized as a cultural heritage is batik that uses hot wax (batik wax) with the main equipment
called canting tulis, batik stamp, or a combination of both [1]. The artificial batik is intended to
include color printing, color removal printing, cold wax printing or a combination of several of these
techniques. The use of these techniques is mostly to make products resembling batik motif. The
artificial product is indeed similar to batik. Artificial batik products on the market are easier to find.
This is because the price is relatively cheaper. Historically printing batik can be categorized as a
counterfeit product because it is not in accordance with the actual definition and technique of batik
and only attempts to resemble it [2]. Batik printing was indeed introduced with the aim of rivaling the
authentic batik. By resembling original batik, for ordinary people will be fooled and increase the desire
to buy these products. To determine whether the item is a real batik product or a falsified product, a
tool is required for this purpose. Center for Crafts and Batik has conduct research on making the "Batik
Analyzer" software. Design software made using "deep learning" technology .

Machine learning is a subfield of computer science that has the ability to learn new codes and
instructions without being explicitly programmed to do. Machine learning is closely related to the
computational field of statistics that focuses on making predictions or forecasting using computers [4].
Machine learning learn with the data entered and can be applied in many applications. Lawrence
revealed that machine learning has been successfully applied to many things, including speech
recognition, visual object recognition, motorcycle taxi detection and application to other domains such
as drug discovery and genomics [4]. Li and his friends applied TensorFlow to analyze data on
diabetics [5]. One of the frameworks for using machine learning is TensorFlow .

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Tensorflow is a framework for building machine learning. Tensorflow was developed by
engineers and researchers working at google companies. The Tensorflow software library uses a data
flow graph that describes mathematical calculations with nodes and edges. Each node in a graph
represents a mathematical operation, while an edge represents a relationship between nodes [3]. Flow
sensors have several advantages, including the Tensorflow system is very flexible and provides many
models, the Tensorflow architecture is very modular so that it can be used per part or used
simultaneously, applications built using Tensorflow are very portable for use in notebooks, desktops
and even mobile computing platforms. Another advantage is that Tensorflow allows programmers to
maximize existing hardware [4].

2. Materials and Methods
2.1. Materials

The tools needed in this study are computers that have adequate processor and graphic
specifications, digital cameras, android phones, camera lights, and digital microscopes. The sample
fabrics included authentic batik and artificial batik. The number of samples is 1030 samples, consisting
of 254 hand-written batik, 271 stamp batik , 299 batik print imitation, 119 burn-out print batik
imitation, and 88 cold wax batik print imitation. Each sample is given an identity that matches the type
of manufacturing process. Another material used is deep learning software, namely Tensorflow .

2.2. Installing Tensorflow on a computer

High-performance computers are installed TensorFlow, an open source API developed by Google
for Machine learning and Deep Learning, but can also be used for others, such as numerical
calculations. This framework can be used both as a backend, with C ++, and frontend in Python. This
process involves the github fork from TensorFlow object detection. Python is equipped with a
labelling module.

2.3. Collecting and Labelling datasets

Each sample (included authentic batik and artificial batik) collected is then checked by the batik
evaluator to determine the type of batik or type of artificial batik based on the manufacturing process.
Each sample is given an identification. The sample that has been identified is then photographed in
several different places. Image capture is uniformed with a magnification of 60 times, 20 times and a
full frame image. Obtained sample images are 5140 images. The results of taking pictures using a
digital microscope and smartphone cameras are placed in folders according to the clothes type. Each
picture is labeled respectively. The finished label file has an xml extension. Files in the form of xml are
then converted into CSV files so that they can be collected in 1 file that will be used to retrieve image
data from the label that has been given.

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Isnaini : Designing Batik and Artificial Batik Differentiator Applications Using Tensorflow

2.4. Application training

The CSV dataset that was created in the previous step will be separated for the training test. The
next process includes label settings, configuration settings, etc. The decisive stage is in the object
detection training stage.

2.5. Testing models using images

At this stage we test the software to recognize the images that we provide well, whether authentic
batik or artificial batik.

2.6. Changing applications to Android apps

This step is carried out so that the application can be run on smartphone devices. Applications
that was built using Windows computers need to be changed to Android apps so they can be run on
smartphones. The computer training step produced models in the protobuf/.pb format. In order that
the model in the format protobuf/.pb can be run on Android phones, it needs to be converted to tflite
format using tfliteconverter.

3. Results
The Batik Analyzer application is designed to run using a smartphone. Smartphones can be

carried everywhere because they are lighter and everyone can have them. This apps is made simple so
it's easy to use. Before taking a picture, the user is asked to sign in or log in first, as shown in figure 1.a.
After the user log in, the user is asked to select the method of shooting, such as 20x magnification or
60x magnification using a digital microscope, or will taking a picture with a smartphone camera. This
is shown in figure 1.b.

(b) 294
(a)

Figure 1. (a) Display sign in user; (b) Display of picture mode selection

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Isnaini : Designing Batik and Artificial Batik Differentiator Applications Using Tensorflow

After shooting is complete, the application will analyze the results of fabric type predictions as
shown in Figure 2. The application displays the results in the form of product types and percentage
predictions.

Figure 2. Display the results of batik image analysis

4. Discussion
The results of batik magnification using 20x and 60x digital microscopes found traces that can be

used to be able to distinguish the manufacturing process. The process of 'nembok' that using the hot
wax is not perfect because of the limitation of the wax resist causes batik to be distinguished from
artificial batik. Among them is the presence of color seepage. These are shown in Figure 3.a and 3.b.

(a) (b)

Figure 3. (a) Magnification of imitation batik; (b) Magnification of batik

In Figure 3.a, it can be seen that at the magnification of the image using a digital microscope, the
imitation of color print batik has no color permeation for the motif. Whereas in Figure 3.b there are a
color permeation that occurs due to the imperfect wax blocking process. This can be used as a

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distinguishing feature between batik and imitation batik (artificial products). The batik analyzer
application is trained to learn traits that can determine products according to their own abilities.

The application trials that have been built to analyze batik products and artificial products are
shown in table 1.

Table 1. Results of trial accuracy on several types of fabrics.

Product type Magnification Magnification Without
20 x 60 x magnification

Hand-written batik 64% 92% 87,5%
Stamp batik 68% 100% 70%
Color discharge printed imitation 32% 48% 80%
Cold wax printed imitation 24% 76% 60%

From the table, it can be seen that the application that built has the highest accuracy at 60x
magnification. However, in the case of color discharge printed imitation, the accuracy is still low. This
is possible because the color discharge printed imitation was combined with batik techniques. So the
application is still confused to recognize between color discharge printed imitation and authentic
batik.

5. Conclusions

Based on the results, it can be concluded that Batik Analyzer Apps using TensorFlow that has
been developed is hoped to be used as an app to help distinguish authentic batik product and artificial
batik product.

References

1. Masiswo, et. al. Karakteristik fisik produk batik dan tiruan batik. Dinamika Kerajinan dan Batik. 2017, Vol
34, No 2

2. Safira, Anya. Et al. Pengaruh faktor sosial, kepribadian, dan demografi terhadap sikap serta intensi
pembelian batik printing’. 2013. Program studi manajemen, Fakultas Ekonomi, Universitas Indonesia.

3. Brooks, T. N. et al. ‘Cuttlefish: A Library For Building Elastic Distributed Neural Networks’, in Proceedings
of Student-Faculty Research Day, CSIS,. 2017. Pace University. New York, pp. C3-1. Available at:
http://csis.pace.edu/~ctappert/srd2017/Proceedings/2017ProceedingsForPrint.pdf.

4. Lawrence, J. et al. Comparing Tensorflow Deep Learning Performance Using CPUs, GPUs, Local PCs and
Cloud, in Proceedings of Student-Faculty Research Day, CSIS, .2017. Pace University. New York, pp. C1-1.
Available at: http://csis.pace.edu/~ctappert/srd2017/.

5. Li, K. et al. .A Deep Learning Platform For Diabetes Big Data Analysis’, in adwanced technologies &
Treatments for Diabetes Conference. 2018. Vienna, Austria. Available at: http://www.liebertpub.com/.

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Quality Analysis of Implementation of Cleaner
Production at Batik Industry Using the Importance

Performance Analysis (IPA) Methods as a Waste
Management Tool

Lilin Indrayani 1, Agus Haerudin 2, Yudi Satria 3
1 Centre of Handicraft and Batik, Yogyakarta

* Correspondence: [email protected]; Tel.: -

Abstract : The implementation of cleaner production must be implemented in order to protect the
environment due to an increase in the quantity of batik waste due to the increasingly rapid growth of
batik industry. Batik industry needs to measure and analyze the extent to which the production
process has implemented cleaner production and evaluates its shortcomings for future
improvements. In this study the calculation using the Importance Performance Analysis (IPA)
method is applied to determine the importance and performance of the batik group industry at
Sleman Regency. This approach aims to assist the batik industry group in evaluating of
implementation of their cleaner production in the production process so that from the integration it
can be seen what needs to be prioritized to be improved as a corrective action. From the research
results, several corrective actions, waste management, water minimization and the reused wax
process can be continued to be a corrective action.

Keywords: cleaner production; batik industry group; Importance Peformance Analysis (IPA)

ISBN : 978-623-91916-0-3

1. Introduction

UNESCO has awarded that batik is Indonesia's national cultural heritage followed by an increase
in the quantity of the batik industry and the growth of new batik industry centers. Batik industry
sector has a strategic role in development especially to foster the level of employment and its
contribution in encouraging national economic growth. Along with the increase in the batik industry,
environmental problems are also increasing. These problems are mainly due to dissipation among
other things in the production process which often results in waste of raw materials, water and
energy and the disposal of waste that will burden the environment. Nowadays, the application of
waste management tools leads to improve to minimize the emergence of an integrated and systematic
manner by all interested parties towards achieving a balance of environmental, economic and social
aspects. Because it is very necessary for a form of waste management equipment that can be applied
to the batik industry, considering it is a challenge to implement an appropriate waste management
effort in small industries as batik industry.

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Analysis (IPA) Methods as a Waste Management Tool

One of the preventive waste management tool is the application of cleaner production. The term
of cleaner production was introduced by the United Nation Environment Program (UNEP) in May
1989 and was formally submitted in September 1989 at a seminar on The Promotion of cleaner
production at Canterbury. Indonesia agreed to adopt the definition conveyed by UNEP (2003)
namely, cleaner production is a preventive and integrated environmental management strategy that
is applied continuously to the production process, products and services so as to increase cleaner
production and reduce the risk of human occurrence and environment. Therefore the strategy needs
to be applied continuously to the production process and product life cycle with the aim of reducing
risks to humans and the environment (Nastiti, 2009). Meanwhile, according to the United Nation
Industrial Organization (UNIDO, 2002), adding that cleaner production is an environmental
management strategy that is directed towards prevention and integration so that it can be applied
throughout the production cycle. The two definitions above have the same goal, namely to increase
productivity by providing a better level of efficiency in the use of raw materials or raw materials,
energy, water and encourage better environmental performance through reducing generation of
waste and emissions sources and reducing impacts products for the environment from the life cycle
of products designed to be environmentally friendly and effective cost.

Before the concept of cleaner production began to be developed, initially environmental
management was based on a carrying capacity approach due to the limited natural carrying capacity
to neutralize increasing pollution. Efforts to overcome pollution problems change the approach of
processing waste that is formed (end of pipe treatment). The concept of cleaner production is a
concept that has a hierarchy where the principle of 5 (five) R (Rethink, Reduce, Reuse, Recycled,
Recovery) must be done directly (in-pipe recycle). So that the resolution of environmental problems is
emphasized at the source of pollution not at the end of the process such as the end-of-pipe treatment
technology, including the efficient use of natural resources which also means shrinkage of waste
produced, pollution, and risk reduction for human health and safety. This concept does not always
require expensive or sophisticated technology but often results in potential savings that increase
competitiveness in the market. What is needed is a change of attitude, responsible environmental
management and assessment of technology choices.

Some of the concepts in the cleaner production strategy introduced in the batik industry include
the following (Bohnet M, 2010):

 Reducing or minimizing the use of raw materials, water and energy and avoiding the use of
hazard materials and reducing the formation of waste at the source, so that it prevents or reduces
the problem of pollution and environmental damage and the risks to humans.

 Changes in patterns of production and consumption apply both to the process and the products
produced, so it must be understood correctly the analysis of product life cycle.

 Cleaner production cannot be successfully implemented without any changes in mindset,
attitudes and behavior of all parties involved both from the government, the community and the
industrial world. Besides that, it is also necessary to apply management patterns among industry
and government by considering environmental aspects.

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Analysis (IPA) Methods as a Waste Management Tool

 Implementation of environmentally friendly technology, management and standard operating
procedures in accordance with established requirements. These activities do not always require
high investment costs; even if they occur often the time needed to return capital investment is
relatively short.

 The implementation of this cleaner production program is more directed at self-regulation and
arrangements that are consensus-based or negotiated regulatory approach. So the
implementation of a cleaner production program does not only rely on government regulations,
but it is based on awareness of behavior change.

This paper will present an analysis of the measurement of the quality of the application of
cleaner production in the Mantaran batik group and Ayu Arimbi batik group at Sleman Regency. The
two batik group industries were chosen as partners in the implementation of cleaner production
carried out between the Center for Crafts and Batik (BBKB) in collaboration with the Department of
Industry and Trade of Sleman Regency. The basis for implementing the cleaner production program.
It is because of the growth of the batik industry every year since the emergence of the Sleman batik
motif which is the Sleman batik icon, the motif of "Parijoto Salak". Along with the development of
these batik industries centers, there is concern about the potential for environmental pollution due to
an increase in the quantity of batik waste. Then through the implementation of sustainable cleaner
production, it is expected that the use of raw materials, the volume of waste water and utilization of
energy can be reduced. This reduction will reduce costs production so that the batik industry in
Sleman Regency can compete with other batik industries. The other important thing is to reduce the
impact of environmental pollution and the health of workers and the community due to the
production process in the batik industry.

The measurement of the quality of the application of cleaner production in the two industry
groups was carried out by the Importance performance Analysis (IPA) method. IPA is a method that
maps the perceptions of group members in each batik industry to the importance of aspects of the
implementation of cleaner production by group members in each batik industry to performance
aspects of the cleaner production program and to identify the applications that have been made in the
batik industry and things that need to be improved. Science is a method used to analyze the
relationship between importance and performance and the theory that the target level of performance
of certain product attributes must be proportional to the interests of these attributes. In other words,
interest is seen as a reaction from the relative values of various consumers attributes (Slack, 1990).

The data presented in the questionnaire are elaborated on the Likert scale as an indicator of size
scale for the benefit according to members of the Mantaran batik group and batik group Ayu Arimbi
Plalangan at Sleman Regency on the implementation or real performance of the program to
implement cleaner production. Likert scale data is given a quantitative score to be used in calculations
(Rangkuti, 2008), this concept actually originates from the SERVQUAL concept, essentially, as
suggested by Parasuraman (Rangkuti, 2008) the level of importance of group members in each batik
industry is measured in relation to what should be done by members of the Mantaran batik group
and batik group Ayu Arimbi Plalangan at Sleman Regency.

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Analysis (IPA) Methods as a Waste Management Tool

Based on the results of the assessment of the level of importance and level of performance,
calculations will be made regarding the level of importance and level of performance which is then
illustrated in a Cartesian diagram. The level of importance and performance contained in the
Cartesian diagram is in the form of scores of importance assessment and total performance. Each
attribute is positioned in a diagram. The total assessment of the level of performance shows the
position of an attribute on the X axis while the position of the attribute on the Y axis is indicated by
the total score of importance on the attribute. (Rangkuti, 2003). The horizontal axis (X) will be filled by
the level of implementation score, while the upright axis (Y) will be filled by the importance level
score. The principle of science calculation is carried out mapping into 4 quadrants for all variables
that affect service quality. Distribution of quadrants in IPA can be seen in figure 1

Figure 1. Importance Performance Analysis Performance (IPA) Diagram

The strategies that can be carried out regarding the position of each variable in the four quarters
can be explained as follows:

a. This Quadrant 1 (Concentrate These) is an area that contains factors that are considered important
by members of the batik group, but in reality these factors are not in accordance with the
performance of members of the batik group (the level of satisfaction obtained is still low). The
variables included in this quadrant must be increased.

b. Quadrant 2 (Keep Up The Good Work) This is an area that contains factors that are considered
important by members of the batik group, and the factors considered by members of the batik
group are in accordance with what they feel so that the level of satisfaction is relatively higher.

c. Quadrant 3 (Low Priority) is an area that contains factors that are considered less important by
members of the batik group, and in reality the performance is not too special. The increase in
variables included in this quadrant can be reconsidered because the effect on the benefits felt by
members of the batik group is very small.

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Lilin: Quality Analysis of Implementation of Cleaner Production at Batik Industry Using The Importance Performance
Analysis (IPA) Methods as a Waste Management Tool

d. Quadrant 4 (Possible Overkill) is a region that contains factors that are considered less important
by members of the batik group and they are felt to be too excessive. The variables included in
this quadrant can be reduced so that batik groups can implement cleaner production.

Based on the results of the measurement analysis using the IPA method, figures will be obtained
which show the quality of the application of cleaner production in the two batik industries. Quantity
data will present the gap value between the perceptions and expectations of members of the
Mantaran batik group and Ayu Arimbi batik group after obtaining the implementation of a cleaner
production program. The data can be used to propose for priority corrective actions that can be taken
to improve the quality of the implementation of cleaner production in the Mantaran batik group and
Ayu Arimbi batik group.

2. Experimental
2.1. Materials

The material in this study is a questionnaire in the form of a likert scale of interest based on
attributes consisting of five-dimensional attributes of the implementation of a cleaner production
program, namely the use of materials, hazardous waste, storage and handling of materials, water and
waste water, energy, protection of occupational safety and health. This questionnaire consists of
general data statements regarding the implementation of cleaner production which is carried out
daily by the two batik industries above. In more detail the indicators are contained in the questions in
the form of a questionnaire distributed to respondents, namely members of the second group of the
batik industry in order to obtain answers related to the matter under study.

2.2. Method
2.2.1 Research Design

The research design in this paper is a type of survey research. In survey research, primary data
collection methods provide questions to individual respondents. This study uses a quantitative
descriptive approach based on user-approach, namely the approach that states that the program
implemented has high quality is a program that satisfies user expectations. Therefore, the gap
analysis model is specifically designed to see the gap between the expectations and perceptions of
members of the Mantaran batik group and Ayu Arimbi batik group. This method is seen as a fairly
appropriate model for analyzing and measuring the level of quality towards the application of cleaner
production activities. While the IPA model can be seen there are two main attributes that determine
user satisfaction, namely expectation and perceived performance. Expectation is the user's
expectation of the desired product. While perceived performance is the perception of members of the
batik industry group on their environmental performance. They measured through five dimensions
of the quality of the application of cleaner production, namely the efficiency of the use of materials,
waste materials or hazardous, storage and handling of materials, water and waste water, energy,
protection of occupational safety and health.

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Analysis (IPA) Methods as a Waste Management Tool

Tabel 1. Dimensions and variables of cleaner production program

No Dimension Variabels
1 Efficient Use of
a. Monitoring of waste produced in the batik industry
Materials and environment
Environmental
Impact Assessment b. Determination of waste segregation system
c. Provision of waste collection points
2 reduce, reuse, d. Determination of waste reduction system
recycled and recovery e. Determination of waste reduction and rejection for
of material and waste
improper materials system
Storage, Handling f. Provision of qualification for reject product
and Transport of a. Environment reduce, reuse, recycled of material and
Materials
waste systems
4 Reduction of Water b. Environment recovery of material and waste
Consumption, Waste
water and Pollution systems
c. Waste management
5 Reduction of Energy d. Waste analyzed from acreditabled laboratory
Consumption, Use of a. Check the quality of raw materials and primary
Heat and
Environmentally products after they are received from suppliers.
Friendly Energy b. Provision of safe storage for hazardous materials.
c. Determination of the system of providing the
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d. Establishment of a system to avoid loss of raw

materials during storage.
e. Spill reduction and leakage
f. Perfect the way to move and to avoid losing

material
g. Ensure good ways for cleaning and disposal of

hazadous packaging
h. Ensure how to avoid losing finished products

during storage and transportation
a. Monitoring water consumption in the batik industry
b. Determination of the system reduces water

consumption in the production process
c. Principle of optimizing water use
d. Determination of the system of reuse and or recycle

water in the production process
e. Establishment of a system to reduce water

consumption in the production room.
f. Determination of the system to reduce water

consumption in non-production parts.
g. Determination of ways in waste water is processed

in a way that is good in terms of the environment..
a. Monitoring energy consumption in the batik

industry
b. Reducing energy consumption and costs
c. Establish ways to avoid losing energy
d. Adequate electrical equipment installed

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Sources e. Calculation of energy consumption in the
production process is tailored to the needs.
6 Protection against
accidents, hazardous f. Reuse of energy in the production process
materials, odors, g. Providing adequate lighting and saving energy
noise, vibration and a. Reduction of accident risk
injury b. Procedures to ensure that machines and equipment

do not cause risks to employees
c. Procedures to ensure that the work environment is

safe for employees
d. Ease of access to information about hazardous

material
e. Availability of PPE equipment (personal protection

equipment) handling hazardous material is
available to employees and well maintained
f. prepare for an accident
g. Fire reduction
h. Reduction of workers' health risks
i. Control of air emissions
j. Odor pollution control

2.2.1 Place and Time of Research

This research was conducted at the Mantaran batik group and Ayu Arimbi batik group at
Sleman Regency. Time of Research This research was conducted in March-July 2018.

2.2.2 Sample research

The population in this study was all group members in the two batik industries who
implemented a cleaner production program totaling 50 members from the two batik industry groups.
The results of data collection were then analyzed using science. The first stage in science is to
determine the level of suitability between the level of importance and the level of performance of the
quality dimensions and variables studied through a comparison of performance scores with scores of
interests. The suitability level formula used is (Santoso, 2011):

Tki = (Xi / Yi)x100% ……………….. (1)

Where : level of conformity
Tki = performance rating score
Xi = importance rating score
Yi =

The second stage is calculating the average for each attribute perceived by consumers, using the
formula :

Where:  Xi = Xi / n Yi = Yi / n ………………… (2)
Xi =
The average score of the performance level of the batik industry

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Yi = The Average score of importance on the batik industry
n = The Quantity of Respondent

Next, the average of all the attributes of importance (Y) and performance (X) are bounded in the
Cartesian diagram, using the formula:

X=  Xi / k Yi / k
Y = …………(3)

Where : The average score of the performance level of the batik industry
X= The Average score of importance on the batik industry
Y= Amount of dimensions can affect the performance of implementing cleaner production
k=

The last stage is the elaboration of each attribute in the Cartesian diagram to see a description of
the quality of the application of cleaner production for each batik group as shown in Figure 1.

3. Results

Level Analysis of Importance and Performance Analysis (Performance)

After calculating the average answer to the questionnaire used in this study, the next step is to
process data and analyze data. Data analysis carried out in this study is to be able to present a
collection of measurement data into readable and useful information. The data has been obtained
from the results of the respondents' assessment of the importance and performance of each group
member towards the implementation of the cleaner production program in the two batik industries
above. The level of conformity is the result obtained from a comparison between performance scores
and interest scores.

The level of conformity is the result of a comparison between the performance performance score
and the interest score, so that it can be used to determine the priority scale (Yola and Duwi, 2013). The
level of importance analysis and the average performance of perceptions of each dimension is the
basis for determining whether each performance of the implementation of cleaner production in both
batik industries is good or not, that is by comparing the average of all attributes (X) and the results of
3. 32 The average expectation of each attribute is the basis for determining whether the attribute is
important or not important, that is by comparing the average of all attributes (Y) and the results
obtained at 4.19 for the batik group Mantaran and X = and Y = for Ayu Arimbibatik group. The mean
values of perception and expectation were used to analyze the data in the Cartesian diagram in
Figure 2 for the batik transfer group and figure 3 for the Ayu Arimbi batik group.

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Quadrant 1 Quadrant 2

Quadrant 3 Quadrant 4

Dimension Dimension Dimension Dimension Dimension Dimension
1: 2: 3: 4: 5: 6:

Figure 2. IPA Cartesian diagram for Mantaran batik Group

Quadrant 1 Quadrant 2

Quadrant 3 Quadrant 4

Dimension Dimension Dimension Dimension Dimension Dimension
1: 2: 3: 4: 5: 6:

Figure 3. IPA Cartesian diagram fork Ayu Arimbi Batik Group

Quadrant 1 is a quadrant that has a very low level of satisfaction so it is a top priority for
improvement. Of the six dimensions included in quadrant I for the Batik Mantaran group, according
to the order of priority levels are the efficiency dimensions of material use and the assessment of
environmental impacts especially for recording systems for recording waste and failed products and

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the absence of a system to minimize waste. Whereas for the Ayu Arimbi batik group is reduction,
reuse, recycling, recovery of waste materials or waste and waste management, especially for the
processing of wax recycling and the determination of a good natural dyestuff system and recording.

Quadrant 2 is the quadrant expected by the two batik groups and is in accordance with what is
felt by the members of their respective groups. Of the six dimensions in quadrant 2 can also be sorted
according to the priority level to be maintained by the Mantaran batik group or Ayu Arimbi batik
group has similarities namely storage, handling, and transportation of materials, reduction of water
consumption, waste water, and pollution, reduction of energy consumption, heat consumption and
environmentally friendly energy sources.

Quadrant 3 is a low priority quadrant because it contains dimensions that are considered less
important by the two batik groups and in reality their performance is not too special, namely the
order of dimensions according to the priority level to be repaired is protection against accidents,
hazardous and toxic materials (B3) odor, noise, vibration and injury.

Both batik groups have similarities in quadrant 4, which does not have a low level of importance,
but has a high level of performance.

4. Discussion

In the concept of cleaner production, it is known that understanding about Non-Product Output
(NPO) is the first step in conducting analysis before applying the concept of cleaner production itself.
NPO is defined as all materials, energy, and water used in the production process but not contained
in the final product. NPO forms can be identified in the batik industry, among others, are less quality
raw materials in the form of poor quality mori dyes, products that fail because they are not in
accordance with the specifications of ordering, reprocessing, solid waste in the form of batik wax ,
liquid waste (amount of contaminants, overall water not contained in the final product), energy (not
contained in final products, such as steam, electricity, oil, diesel, etc.), emissions (including noise and
odor), losses due to lack of maintenance and losses due to health and environmental problems, NPO
total cost is the sum of NPO costs from inputs, NPO costs from the production process, and NPO
costs from output. In general, the total cost of NPO ranges from 10-30% of the total production cost.
Therefore it is important for the batik industry to recognize materials sources in the production
process in detail, in order to reduce production costs and increase productivity.

Through the results of the IPA calculations of the two batik groups, from the six dimensions it
can be concluded that several things that can be used as a reference for corrective actions, especially
the variables in the Batik Mantaran group, are dimensions of efficient use of materials and assessment
of environmental impacts, especially for recording systems for recording waste and products failed
and the absence of a system that was put in place to minimize waste. From these data, it was known
the problems faced by the batik industry which are members of the Mantaran batik group and Ayu
Arimbi batik group at Sleman Regency, among others, is that there has been no detailed identification
and calculation of sources of waste of resources. This is because one component of the resource, for
example, water for the production process uses water from Serayu river. Whereas wood as a source of

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heat energy in release wax (pelorodan) process is available at cheap prices from wood collectors
around it. Although the process of recycled wax with the process of recycled wax collection has been
carried out but only limited of being resold to use wax collectors at a price of approximately 30
percent of the price of a fresh wax on the market. The recycled wax process for reuse in the
production process has not been done so that because there is no value of production cost savings
from recycled wax use so NPO is also dissolved in wastewater. The absence of traps of recycled wax
is wax trap (koen). Chemical management has not been carried out optimally by reusing used dyes so
that the cost of consuming dye cannot be reduced and increasing the amount of liquid waste used in
the coloring process. The environmental load is very large because of the unavailability of waste
water treatment plant for good waste treatment.

5. Conclusion

The application of cleaner production is a series of work procedures that are applied at each
stage of batik production. The work procedure is expected to result in changes in the mindset and
consumption patterns of resources, raw materials and waste management so as to reduce
environmental pollution. The results of calculations with the IPA method can be used several
variables used as stages of improvement.

It expected for Mantaran batik group and Ayu Arimbi batik group at Sleman Regency can apply
the principle of cleaner production continuously so that it gets production efficiency and can reduce

production costs also can minimize the potential for environmental pollution and public health.

References

1. Center for Crafts and Batik. Activity Report, Yogyakarta, 2017,
2. Bohnet et al. Ullmann's Encyclopedia of Industrial Chemistry, WILEY-VCH, 2010. CBI. Application of Clean

Production in the Batik industry, KLH, Jakarta, 2010.
3. P. Eimer et al. Good Internal Management (Good Housekeeping), GTZ / P3U, Bonn, 2003.
4. J. Miller. et al. Chemical Management Guide: Improve Chemical Management to gain Cost Savings, Reduce

hazards and Improve Safety, Revised ed. GTZ., Eschborn, 2003.
5. I. Nastiti Siwi and F. Anas Miftah, Clean Production, IPB Press, Bogor, 2009.
6. UNEP .Division of Technology, Industry & Economics, Environment Agreement and Cleaner Production, 2007.
7. UNIDO. Joint UNIDO-UNEP Program on Resource Efficiency and Cleaner Production (RECP), 2002
8. Santoso. (2011). Consumer Perception of the Quality of Telo Bakpao with the Importance Performance Analysis

(IPA) Method. Journal of Agricultural Technology.12 (1)
9. Yola, M dan Budianto, D. (2013). Analysis of Consumer Satisfaction on Service Quality and Product Prices in

Supermarkets Using the Importance Performance Analysis (IPA) Method. Industrial Systems Optimization
Journal. 12 (12) : 301-309.

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Potential Indonesian Kenaf Varieties (Kenafindo) as
Textile Raw Materials

Elda Nurnasari 1*, Nurul Hidayah 1, and Nurindah 1

1 Balai Penelitian Tanaman Pemanis dan Serat

* Correspondence: [email protected]; Tel.: -

Abstract : Kenaf is a natural fiber-producing plant that is widely used as a raw material for making
fiberboard (door-trim, car interior), particle board, fiber drain, geo-textile and high-quality paper.
Kenaf (Hisbiscus cannabinus) produces fiber from its trunk, which is then classified as a bast fiber.
Two new superior varieties of kenaf, Kenafindo 1 and Kenafindo 2 have the advantage of being less
sensitive to the length of the daylight. Potential production of Kenafindo 1 and Kenafindo 2 is 2.5-4.5
tons of dry fiber / ha with fiber length of 225-235 cm, white fiber color (grade A), and fiber
smoothness (smooth). Kenaf fiber lignin levels of Kenafindo 1 and Kenafindo 2 varieties were 9.36%
and 7.26% respectively. Kenaf fiber Kenafindo 1 and Kenafindo 2 varieties have the opportunity as
textile raw materials for garments. There are several advantages for using kenaf fiber as textile raw
materials including good absorption, fire resistance and antimicrobial properties.

Keywords: natural fiber; kenaf; potential; textile

ISBN : 978-623-91916-0-3

1. Introduction

The textile and textile products industry (TPT industry) is one of the pioneering industries and
the backbone of the industrial sector in Indonesia. However, the import of cotton fiber, the textile raw
material, reached up to 450 thousand tons in 2016 (Dirjen Perkebunan, 2017). During the past decade,
the government through the Ministry of Industry tried to explore the potency of Indonesia's natural
resources known as SANT (Non-Textile Natural Fiber) such as pineapple fiber, kapok fiber, coconut
fiber, banana fiber, fiber from doyo caterpillar and so on, to be used as raw material for textiles and
textile products (Sukardan et al., 2016).

Natural fibers are obtained from nature, plants, animals and mineral sources. Natural fibers are
categorized into two main groups: cellulose or plant fibers; and animal protein or fiber. Fiber is used
as raw material for making yarn, which is then woven, knitted or tied into a cloth used in clothing
production. Fiber-producing plants are divided into three groups, namely fruit fiber plants, stems,
and leaves, based on plant parts that produce fiber. Kenaf is a plant that belongs to the group of stem
fibers because kenaf fibers are obtained in the bark (Sudjindro, 2011). Kenaf fiber has been developed
in Indonesia since 1978 through the People's Sack Fiber Intensification (ISKARA) program. At that
time, kenaf fiber was only used as a gunny sack (Aghnat Baizura Hapidh, 2017)., Kenaf fibers are

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Elda: Potential Indonesian Kenaf Varieties (Kenafindo) as Textile Raw Materials

classified as sclerenchymal fibers, in which the highest fiber content (75%) is found in the lower stem
and known as thick-walled cells containing lignin. This fiber functions mechanically so that it is
resistant to stress due to withdrawal and bending, pressure and compression without causing
damage to the thick-walled cells in this part of the plant (Suliyanthini, 2016).

The kenaf plant has two fiber types: the outer bark or bast portion (40% of the plant) and the
inner woody core material (60%) (Zhang, 2003). Kenaf fibres are produced by separating the core of
kenaf from the fibrous outer layers. The extracted layers are chemically or microbiologically retted to
convert into fibres. Retting is a wet process by which bundles of cells in the outer layer of the stalk is
separated from the non-fibrous material by removing pectins and other gummy substances. Retting
aims to eliminate components that attach fibers, such as pectin, hemicellulose, lignin, and other
impurities without damaging cellulose fibers(Yu and Yu, 2010; Jamil. H and Marjani, 2018).

Kenaf fiber has several advantages including good absorption, fire resistance and antimicrobial
properties, so it can be used as a textile raw material for various purposes. Kenaf fiber is also not
affected by moisture and insulated against noise and heat (Ramakrishna G et al., 2018). Kenaf fibres
are found as useful applications in making knit and woven textiles. These fibers blend very well with
cotton, and find its commercial and healthy applications in making outerwear due to its natural
absorbency and fire-retardant abilities(Fibre2fashion, 2019).

Kenaf fiber also has several disadvantages when used as a textile raw material. Kenaf bundle
fibers are rather stiff, and the single fibers are also relatively short for textile yarn manufacturing
processes. These unfavorable properties have inhibited the production of high-quality yarns and
fabrics containing kenaf (Zhang, 2003). Therefore, some research have provide combined kenaf fiber
with cotton fiber to be proceeded into yarn as textile raw material (Ramakrishna G et al., 2018). The
combination of kenaf fiber and other types of fiber plants makes the fiber more qualified and suitable
to be used as textile raw materials. This paper elaborates the potency of two Indonesian kenaf
varieties (Kenafindo 1 and 2) as textile raw materials and discusses the characteristics of fiber quality
and chemical properties of these varieties.

The Fiber Morphology of Kenafindo 1 Agribun and Kenafindo 2 Agribun

The quality of the raw material product can be assessed from its morphology and characteristics.
Data on the characteristics of Kenafindo 1 and 2 Agribun fibers are presented in Table 1. In 2017, the
Indonesian Research Institute for Sweeteners and Fiber Crops released two new superior varieties of
kenaf namely Kenafindo 1 Agribun and Kenafindo 2 Agribun. Kenafindo 1 Agribun variety is the
result of a cross between a clone (G4 x KK60) x G4 continued with pedigree selection (SK Release of
Kenafindo Variety 1 Agribun, 2017). While the Kenafindo 2 Agribun variety is the result of mass
selection in the population IDN-09-HCAN-1272 (SK Release of the 2nd Agribun Kenafindo Variety,
2017).

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Table 1. The fiber characteristics of Kenafindo 1 Agribun and Kenafindo 2 Agribun

Parameter Value

Kenafindo 1 Agribun Kenafindo 2 Agribun

Fiber grade AA

Fiber color White White

Fiber length (cm) 260 - 375 255-370

Fiber strength (g/tex) 22,19 – 28,89 (very good) 22,96 – 29,36 (very good)

Fiber glossy Glossy Glossy

Dirt little little

Fiber smooth Smooth Smooth

Fiber yield (%) 5–7 5–7

Potential yield (ton/ha) 2,75 – 4,50 2,50 – 4,50

Source : SK Pelepasan Varietas Kenafindo 1 dan 2 Agribun, 2017

According to Ciptandi (2014) in Hapidh (2017), kenaf fiber is divided into three grades, which
can be seen from the characteristics of each fibers. The following are the types of kenaf fibers,
including:
a. Grade A grade kenaf fiber, special fiber quality (good). The A-grade-kenaf fiber has the

characteristics of soft, shiny, clean from gum/sap, easy to decompose, brilliant white color and
completely clean from dirt.

b. Grade-B kenaf fiber, good quality fiber. This B-grade kenaf fiber has the characteristics of fibers
that have good softness and luster, still contain little gum / sap, are relatively difficult to decipher,
dull white, and sufficient fiber cleanliness.

c. Grade C kenaf fiber, poor quality fiber. This C-grade kenaf fiber has the characteristics of coarse
and rigid fibers because it mixes with the bark which is not perfectly processed when retted,
tangle, hard to decipher, brittle, blackish brown and dirty.

Kenafindo 1 and 2 Agribun fibers have grade A quality and are white, so the quality of this fiber
is very good and is suitable to be used as a thread for textile raw materials. In terms of physical
appearance, kenaf fiber has a glossy luster and fine fibers. Kenaf fiber also contains a small amount of
impurities. Therefore, kenaf fiber is potential to be used as textile raw material. Based on the research
of Ciptandi et al., (2014) the quality of grade A kenaf fibers has a homogeneous filament length. Data
on the physical properties of kenaf fibers are presented in Table 2.

Table 2. Mechanical property of kenaf fiber

Standard quality Quality of kenaf fiber Reference
Tensile strength
Softness 240-930 MPa Alias et al., 2018/ (Ciptandi et al., 2014)
Elongation
Fibre Denier 5 – 10 denier Ciptandi et al., 2014
Tenacity
Breaking elongation Max 5 % Ciptandi et al., 2014

14-33 Franck, 2005

2.4-3.33 (g/d) Ramarad, 2008

1.6 % Franck, 2005

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Moisture regain 10-20 % Yu et.al, 2010

Based on Table 2, kenaf fibers have a tensile strength of 240-930 MPa (5-15 g / tex) and this tensile

strength is almost similar to cotton fiber which is 287-597 MPa (18 g / tex) (Alias et al., 2018 ; Wakelyn

et al., 2006). The strength of fiber is the energy needed to break a bundle of fiber in the size of one unit

with a stelometer which is expressed in the ratio between the strength at breaking and stretching.

Fiber strength is expressed as gram per tex. One tex unit is the weight of 1000 m of fiber expressed in

grams. The strength of fiber (cotton fiber) received by the textile industry is at least 28 g / tex,

(Abdurrakhman, 2012). The strength of Kenafindo 1 and 2 fibers is ranging from 22.19 - 29.36 g / tex

(Table 1), so most likely it can be accepted in textile industry.

Softness or smoothness of fiber is expressed in denier units. The size of the filament yarns is
determined as denier, which is expressed in terms of weight per unit length. If 9000 meters of yarn
weigh 1 gm, it is counted as 1 denier. In this system, the unit of length remains constant. The finer the
yarn, the smaller the number (Commercial Garment Technology, 2016).The elongation of cotton is
expressed as percent elongation taken at the point of breaking, hence the term elongation at break.
For most cotton, elongation at break, or just elongation, ranges from 6–9%. The effect of moisture is
most pronounced on elongation. An elongation of about 5% at low relative humidity will increase to
about 10% when the relative humidity is almost at the saturation point (Wakelyn et al., 2006). The
kenaf fiber elongation is 5% which is similar to cotton fiber.

Tenacity is a textile parameter that is related to the strength of a textile material. The smaller the
size, the fiber strength is higher, then the fiber is said to have high tenacity (Wahyudi et al., 2015). The
absorption (moisture regain) of kenaf fiber is quite high (10-20%), higher than cotton fiber which is
only 8%. This higher absorbency makes flax more able to absorb body fluids such as sweat when used
as raw material for apparel textiles (Novarini and Mochammad, 2015). Moisture regain is a
characteristic index of moisture absorption ability in air (humidity), which also reflects the
characteristics of fiber structure. The amount of moisture regain is very important because it deals
with the comfort of a textile material (Wahyudi et al., 2015).

2. Experimental
2.1. Materials

Chemical Characteristics of The Fiber of Kenafindo 1 Agribun and Kenafindo 2 Agribun

The chemical characteristics of fiber are basic information required for using natural fibers in
industrial fields. This relates to fiber-based products or diversification of fiber products, which is
needed in the manufacturing process data regarding these characteristics, including their potency as
textile raw materials (Nurnasari and Nurindah, 2017). The characteristics or chemical properties of
natural fibers derive from plants, including hemicellulose, cellulose, lignin, and extractive substances.
Table 3 shows the chemical characteristics of kenaf fibers. The composition of these substances
generally varies greatly depending on the type or variety of kenaf fibers.

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Table 3. Chemical characteristics of Kenafindo 1 Agribun and Kenafindo 2 Agribun

Value

Parameter Kenafindo 1 Kenafindo 2 Cotton*

Agribun Agribun

Water content (%) 8.36 7.73 8.0**

Ash (%) 0.84 0.74 1.42

Solubility in hot water (%) 1.36 1.91 4.44

Solubility in cold water (%) 0,29 0,70 3.48

Solubility in NaOH (%) 11,62 11,64 5.22

Solubility in Alkohol-Benzena (%) 0.30 1.15 1.66

Lignin (%) 9.36 7.26 -

α-cellulose 63,29 62,78 98.06

Holocellulose 95,94 93,97 93.34

Hemicellulose 32,65 31,19 -

Source : *(Nurnasari and Nurindah, 2017); **(Sukardan et al., 2016)

The water content of Kenafindo 1 and 2 fibers is quite high, 8.36 and 7.73%, respectively, when
compared to the water content of cotton fiber which is 8.00%. Fiber that has quite high moisture
content affect the nature of the yarn or textile produced. Fiber with high water content will have good
comfort when used. In addition, fiber also has good conductivity and absorption capacity for water
vapor (Sukardan et al., 2016). The low water levels may result in decreased fiber strength and easily
broken fibers, which then reduce fiber length (Moerdoko et al., 1973; Saroso and Darmono, 2002).
Kenafindo 1 and 2 fibers have quite high moisture content so that they have almost the same quality
as cotton fibers. It means that they are potential to be used as textile raw materials.

The solubility of fiber in hot water, cold water, NaOH and alcohol-benzene is the extractive
content of fiber. Extractive content is the result of the secondary metabolic processes of plants which
depend on the type, place of growth and climate. Components that are dissolved in cold water are
tannins, gums, carbohydrates and pigments. Whereas those dissolved in hot water are the same as
those dissolved in cold water plus starch component. Soluble substances in alcohol-benzene are
resins, fats, waxes and tannins. Soluble substances in NaOH are lignin, pentosan and hexane
(Fatriasari & Hermiati 2008). Extractive substances have an important role in the nature of wood such
as natural durability, color and odor (Nurnasari and Nurindah, 2017). The solubility of kenaf fibers in
hot and cold water is lower than cotton fibers, this indicates that the amount of solutes such as
tannins, gums, carbohydrates, pigments and starch contained in kenaf fibers is lower than cotton
fiber.

The content of natural fibers generally consists of cellulose, hemicellulose, and lignin. Cellulose
and lignin are among the criteria that show the strength of fiber. Cellulose in composing cell walls is
not in the form of a single molecule but in the form of bonds of around 36 molecules of cellulose
joined by hydrogen bonds forming a beam of elementary fibrils, and elementary fibrils combine to
form linear crystals called microfibrils. Microfibrils combine to form fibrils and eventually cellulose

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fibers are formed (Sjostrom 1993). Lignin increases the resistance of the wall to pressure and prevents
cellulose microfibrils from folding. Cotton fiber does not contain lignin because cotton fibers are pure
cellulose and are classified as fruit fibers while kenaf fibers are classified as stem fibers which contain
lignin (7.26 and 9.36%).

2.2. Method

The Use of Kenaf Fiber as Textile Raw Materials

Textile raw materials derived from natural fibers are currently still dominated by cotton fibers
which belong to the fruit fiber group. The group of stem fibers is still not widely used for textile raw
materials and most are still mixed materials, such as a mixture of kenaf fiber and cotton, or hemp
fiber. The application of stem fibers (bast fiber) is for the needs of textiles and textile products (TPT),
among others, for textiles (napkins, fabrics, handbags, clothing, denim, socks), technical textiles
(ropes, mines, nets, canvas bags, tarps, carpets, geotextiles) (Novarini and Mochammad, 2015).

Kenaf fibers are produced when the core of the kenaf is separated from the fibrous outer layers.
Kenaf fibers tend to be stiff because of the lignin content. In order to convert kenaf fibers into a fiber
for valuable textile products, the fibers must be either chemically or microbiologically retted (Bel-
Berger et al., 1999). Retting aims to eliminate components that attach fibers, such as pectin,
hemicellulose, lignin, and other impurities without damaging cellulose fibers (Yu and Yu, 2010; Jamil.
H and Marjani, 2018).

There are several methods for retting kenaf bars, including wet retting and dew retting. Wet
retting is carried out by soaking the kenaf stems in water. Wet retting has several disadvantages, such
as requiring large amounts of water and producing waste that can pollute the environment because it
causes foul odors (Tahir et al., 2011) and methane gas from the anaerobic respiration process (Banik et
al. 1993; Jamil. H and Marjani, 2018). The second retting method is dew retting which is the process of
retting by placing plant stems on the land and letting the original microorganisms grow, especially
filamentous fungi (Jamil. H and Marjani, 2018).

Kenaf fiber, when used directly as a textile raw material, produce a rigid yarn so it needs to be
mixed with cotton fiber with a certain ratio to produce finer and stronger fibers. Bast fibers, such as
linen and ramie, are typically blended with cotton to improve fabric hand (Cheekand Roussel, 1989).
Kenaf has a good potency to become an excellent source of fiber in the manufacturing of pulp, paper,
and other textile products (Ramaswamy et al., 1995). Kenaf should be potential in the textile industry
in manufacturing fabrics like the ramie/cotton blends (Ramaswamy and Easter, 1997).

After retting, the kenaf fiber is processed with the mercerization method. Mercerization is the
treatment of fabrics or yarns, with an alkali. The alkali causes the fiber walls to swell and become
round, thus increasing in strength, luster, and absorbency. Mercerization also enhances dyeability of
cellulosic fibers (Zhang, 2003).

Previous studies showed that the composition of the mixture of kenaf fiber and cotton is 70%

cotton and 30% kenaf. The composition of cotton fibers should not exceed 30% because the resulting

yarn decreases in strength and is more rigid (Zhang, 2003). The characteristics of kenaf fiber are long,

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strong, good elasticity, shiny, and broken white color, so that it has the potential to be used in the
textile sector to be further developed into textile raw materials and textile products that have
functional and aesthetic values (Hapidh, 2017).

The next steps of using kenaf fiber into textile raw material are weaving (scouring), bleaching,
softening, and continued by the spinning process. Based on the results of research by Indriani and
Widiawati (2013), the spun kenaf fiber has a regular shape and size even though there are still fine
fibers from the fiber. Kenaf fiber is potential to become textile raw material because it can be used as a
thread through a spinning process. The form of kenaf fiber is filamentous, so, without spinning, the
plant fibers can be directly used as threads, but it is only slightly stiff. Kenaf fiber is also suitable to be
used as accessories for fashion products such as bags.

3. Conclusion

Kenaf fiber Kenafindo 1 and Kenafindo 2 varieties have the potential to become textile raw
materials for garments. There are several advantages for using kenaf fiber as textile raw materials
including good absorption, fire resistance and antimicrobial properties. Previous studies showed that
the optimal composition of the mixture of kenaf fiber and cotton is 70% cotton and 30% kenaf.

References

1. Abdurrakhman, 2012. Variasi Karakter Mutu Serat Koleksi Plasma Nutfah Kapas. Pros. Semin. Nas. serat
alam Inov. Teknol. serat alam mendukung agroindustri yang berkelanjutan 67–72.

2. Aghnat Baizura Hapidh, 2017. Eksplorasi serat kenaf sebagai aplikasi produk fashion aksesoris. Fakultas
Industri Kreatif Universitas Telkom, Bandung.

3. Alias, A., Tahir, P., Abdan, K., Salit, M., Wahab, M., Saiman, M., 2018. Evaluation of Kenaf Yarn Properties
as Affected by Different Linear Densities for Woven Fabric Laminated Composite Production. Sains
Malaysiana 47, 1853–1860. https://doi.org/10.17576/jsm-2018-4708-25

4. Bel-Berger, P., Von Hoven, T., Ramaswamy, G.N., Kimmel, L., Boylston, E., 1999. Cotton/Kenaf Fabrics: a
Viable Natural Fabric. J. Cotton Sci. 3, 60–70.

5. Ciptandi, F., Kahdar, K., Sachari, A., 2014. Quality Improvement of Raw Material of Natural Fibre
Preparation using Pectinase Enzyme Case Study : The Harvest of Kenaf Fibre in Laren District , Lamongan
Regency , East Java 6, 36–40.

6. Commercial Garment Technology, 2016. Classification and general properties of textile fibres. pp. 77–184.
https://doi.org/10.1016/S1079-4042(09)04207-6

7. Dirjen Perkebunan, 2017. Statistik Perkebunan Indonesia 2015-2017 Kapas. Sekretariat Direktorat Jenderal
Perkebunan- Kementerian Pertanian, Jakarta.

8. Fibre2fashion, 2019. Ecology, Economy & Equity: Kenaf fibre fabrics [WWW Document].
https://www.fibre2fashion.com/industry-article/6856/ecology-economy-and-equity.

9. Hapidh, A.B., 2017. Eksplorasi Serat Kenaf sebagai Aplikasi Produk Fashion Aksesoris. Bandung.
10. Indriani, I., Widiawati, D., 2013. Eksplorasi Struktur Serat Tanaman Kenaf (Hibiscus canabinus L.) pada

Teknik Tenun ATBM Sebagai Bahan Baku Tekstil. J. Tingkat Sarj. Bid. Senirupa dan Desain 1, 1–8.
11. Jamil. H, A., Marjani, 2018. Efektivitas Retting Embun Batang Kenaf oleh Jamur Pelapuk Putih Trametes

versicolor (L.) Lloyd 10, 82–89. https://doi.org/10.21082/btsm.v10n2.2018.82
12. Novarini, E., Mochammad, D., 2015. Potensi Serat Rami (Boehmeria Nivea S. Gaud) Sebagai Bahan Baku

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Industri Tekstil Dan Produk Tekstil Dan Tekstil Teknik. Arena Tekst. 30, 113–122.
https://doi.org/10.31266/at.v30i2.1984
13. Nurnasari, E., Nurindah, 2017. Karakteristik kimia serat buah, serat batang, dan serat daun. Bul. Tanam.
Tembakau, Serat Miny. Ind. 9, 63–71. https://doi.org/10.21082/btsm.v9n2.2017.63
14. Ramakrishna G, Srinivasan J, Niveda, Gowtham S, 2018. Development of Sustainable Textiles from Kenaf-
Cotton Blended Yarn. https://doi.org/10.31031/TTEFT.2018.01.000514
15. Sudjindro, 2011. Prospek Serat Alam untuk Bahan Baku Kertas Uang. Perspektif 10, 92–104.
16. Sukardan, M., Natawijaya, D., Prettyanti, P., Cahyadi, Novarini, E., 2016. Karakterisasi Serat dari Tanaman
Biduri (Calotropis gigantea) dan Identifikasi Kemungkinan Pemanfaatannya Sebagai Serat Tekstil. Arena
Tekst. 31, 51–62.
17. Suliyanthini, D., 2016. Ilmu Tekstil, 1st ed. PT RAJAGRAFINDO PERSADA, Depok.
18. Wahyudi, T., Kasipah, C., Sugiyana, D., 2015. Ekstraksi Serat Bambu Dari Bambu Tali ( Gigantochloa apus)
untuk Bahan Baku Industri Kreatif. Arena Tekst. 30, 95–102.
19. Wakelyn, P.J., Bertoniere, N.R., French, A.D., Thibodeaux, D.P., Rousselle, M.-A., Triplett, B.A., Goynes,
W.R., Edwards, J.V., Hunter, L., McAlister, D.D., Gamble, G.R., 2006. Cotton Fiber Chemistry and
Technology, Cotton Fiber Chemistry and Technology. https://doi.org/10.1201/9781420045888.fmatt
20. Yu, H., Yu, C., 2010. Influence of Various Retting Methods on Properties of Kenaf Fiber. J. Text. Inst. 101,
452–456. https://doi.org/https://doi.org/10.1080/00405000802472564
21. Zhang, T., 2003. Improvement of kenaf yarn for apparel applications, LSU Master. ed.

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(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Image Color Scale Analysis of Textile Texture Visual
in Indonesia Trend Forecasting

Irfa Rifaah M.Ds 1 *

1 STTT Polytechnic Bandung

* Correspondence: [email protected]

Abstract: Image color scale is one of the methods in the Shigenobu Kobayasi theory. It is uncover
picture or visual media to be studying or analysis that can gave a result a match and correlated
between color combination, color tone or hue, or key word scheme that Kobayashi made basic of his
research. Textile texture visual is a picture in the Indonesian Trend Forecasting (ITF) that use to be
guidance in the design that expected can related with market taste in the future. In the textures
section, ITF gave us a textile texture visual with no details, intended to give audiences idea, or be
inspired with the visual picture. The textile texture visual will be the object of this research with
aesthetic approach, focus on color image scale from Shigenobu Kobayashi theory. The purpose of this
research is description of textile texture based on image color analysis that can be used as strengthen
data for character design themes in ITF, especially in the fashion area.

Keywords: textile texture visual; color image; Indonesian Trend Forecasting (ITF).

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1. Introduction

Indonesian Trend Forecasting (ITF) is a guidance for designers to make a design that expected
can related with Indonesian market taste in the future. Content of ITF are themes of character design,
which is concerning key colors, shapes, styles, patterns and textures. In the textures section for
fashion design guidance, ITF gave us a textile texture visual with no details, intended to give
audiences idea, or be inspired with the picture. The effect of this textile texture visual can be
interpreted in different language of emotion and psychological. In art and design discipline, visual
language can be analysed in aesthetic approach. Aesthetic approach is a study of formal elements
design, such as lines, shape, textures, space, color, light, which composed to make aesthetic appeal.

Color on the other hand, has specific theory related to image or psychology. One color can be
interpreted or express some emotion, two or more than one color combination can give an image and
psychology. Now in the visual world, colors can be provided from photos, illustration, and drawing
which is the effect can be analysed by image color theory. Shigenobu Kobayashi is a leader in the field
of color psychology, gave Key Fashion Words Scheme that related with Color Image Scale Scheme.

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In this research, textile texture visual from each themes in ITF will be analysed with a two
dimensional of color image scale method from Kobayashi theory, based of Hue and Tone Color
System. The result will be reviewed with Key Fashion Words Scheme that related with Color Image
Scale Scheme.

The purpose of this research is description of textile texture based on image color analysis that
can be used as strengthen data for character design themes in ITF, especially in the fashion area.
Because in the ITF and fashion area many adjective languages are used, therefore the result of this
research could be a feedback for ITF. The result of color image scale from object research textile
texture visual could hopefully give a new adjective language base on Kobayashi theory that can be
matched or correlated with the key word of themes, and key word of color palette from ITF themes.

2. Materials and Methods

ITF launched their guidance for 2019/2020 period in April 2019, with tittle Singularity. The
word singularity can be defined as a special, unique, or an odd event as an impact from the
development of artificial intelligent (AI). It is unimageable circumstances as if the people in the
medieval were being explained about internet technology which would be sounded weird and
unexpected. By facing this condition, along comes several unexpected responses poured in four trend
theme concepts: Exuberant, Neo Medieval, Svarga, and Cortex [2] (p.7).

Exuberant theme brings the spirit of Pan Asians-young Asian descendants settled in the USA-
who becomes successful in art and pop culture. It also represents flourishing Grey, the age of age
group consisting of productive, energetic and dynamic people with stylish appearances. Based on
Exuberant background, the fashion style inspired by mix culture between eastern and western, street
art, culinary art, hip hop music, will be looked sporty-casual, with basic shape of clothing yet
unique, mix match. The key colors palette brings cheerful, calming, and maturity. [2] (p.23)

(a) (b)
Figure 1. (a) Exuberant theme visualization; (b) Textile Texture Visual from Exuberant theme.

Neo Medival theme is triggered by the concern of potential World War III, due to conflicting
interest of some countries. That looming concern has conceived imagination similar to futuristic
war movies. Intergalactic, medieval, futuristic, and apocalyptic atmospheres variously influence

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Neo Medieval fashion style. Some are elegant and prestigious, some are feminine yet bold, and
some others are sturdy, reflecting resilience. The shape of look variant: sleek, loose and oversized.
The key color palette gives a dark, heavy, warmer mood, rusty impression. [2] (p.49)

(a) (b)
Figure 2. (a) Neo Medieval theme visualization ; (b) Textile Texture Visual from Neo Medieval
theme.
Svarga is an interpretation of human’s substantial ideology, which is living in harmony, joy,
peacefulness and prosperity on earth. This aspiration is expressed through a warm eclectic styling
with rich elements born from various cultural inspirations. A contemporary style combined with
ethnic inspirations. The silhouette are fitted to loose and oversized, and the touches of traditional
and modern crafts are never missing in the details. Key color representing warm atmosphere of
happiness, even cool colors also appear are processed further to appear warmer. [2] (p.75)

(a) (b)
Figure 3. (a) Svarga theme visualization ; (b) Textile Texture Visual from Svarga theme.

In Cortex theme, the results of AI exploration are applied as algorithmic shapes, such as
fractal form with repetitive lines that creates the sense of growing and moving. The visualized as
arrangement and composition of subtle lines. Cortex’s palette show plenty of cool and pale colors
in pastel tone. Therefore, complex design lines of this theme would appear calmer. [2] (p.101)

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(a) (b)
Figure 4. (a) Cortex theme visualization ; (b) Textile Texture Visual from Cortex theme.
Color image scale with psychological axes colors method from Kobayashi theory has two and
three dimensional scheme, that each psychological axes line represents the character of color. First
axes horizontal line is about the temperature of color, which is warm (W) and cool (C). The second
one is vertical axes line that contain the range of hard (H) and soft (S) the colors, which is related with
the dark, brightness of the color. The last axes (added in three dimensional schemes) is a line or
shapes that through the two axes before, which is contains the clear and grayish color. The third
psychological axes is based on changing sunlight of color.

(a) (b)
Figure 5. (a) Three Dimensional Color Image Scale ; (b) Two Dimensional Color Image
Scale.

The two dimensional color image scale is a method that going to be use in this research,
because the classified color of textile texture visual already complex. The complexation are shown
with the amount of 3-10 colors in one combination from one picture.

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Table 1. Color Image Scale Result of ITF Textile Texture Visual Themes

No. Theme Textile Texture Visual Color Image Scale Result
1. Exuberant

2. Neo
Medieval

3. Svarga

4. Cortex

3. Results

3.1. Analysis based on Color Image Scale

Color Image Scale and key word in Kobayashi theory are related one another. Because by
consulting the key word image scale and using Color Combination Image Scale as base, the
different color combinations can be ordered according to whether they warm or cool, soft or hard,
clear or grayish. [3] (p.10). Based the result in Table 1, each themes result will be replaced in color
image scale that appear in the picture below.

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(a) (b)
Figure 6. (a) Exuberant color image scale area; (b) Neo Medieval color image scale area.

(a) (b)

Figure 7. (a) Svarga color image scale area ; (b) Cortex color image scale area.

3.2. Analysis based Key Fashion Words Scheme
As told before that color image scale and key word in Kobayashi theory are related one

another. According to Kobayasi, they are not only an aesthetic approach in visual color
combination, but also related with the effect image that given by the colors, see figure 8. The
analysis of textile texture visual that already been examined has a scoop of area based Key Fashion
Words Scheme in this table below.

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Figure 8. Key Fashion Image Words

Table 2. Key Words Scheme Result of ITF Textile Texture Visual Themes

No. Match Diagram Key words match
1.
Exuberant Match result
2 image Cheerful
1 Cheerful

Calm Mild (synonym)

3

Maturity Mature (basic word)

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Irfa: Image Color Scale Analysis of Textile Texture Visual in Indonesia Trend Forecasting Match result
Heavy & deep
2. Neo
Medieval Warm
image
Heavy

2

Warm

1 Svarga Match Result
image Warm
3.
Warm
1

4. Cortex Match Result

image

3 Cool Cool

2

1 Pale Young

Calm Mild

4. Discussion

Based on Figure 6 and Figure 7, the result of textile texture visual examined closed enough

with color image scale. Exuberant theme has a vivid tone in their key color palette, but in result of

examine the textile texture visual there is a little bit different of the tone. The result gave us bright

color but not yet vivid or contrast. Neo Medieval theme result is dominant replacement near the

center of axes, because there is so much brown and achromatic color that is one of the neutral

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colors. However, there is a little bit scoop area of result overlapping with vibrant color
combination. Svarga as one of themes that has many colors, match with the color image scale,
because the tone and hue of this theme is bright and vivid. Cortex as the last theme should have
scoop near to cool axes, however the result also indicated cortex was also close to the warm axes.

Key Fashion Words Scheme is one of keywords that Kobayashi gave other than interior, and
other image field. In the Table 2, there is a match keyword of image that is suitable with the theme of
ITF, such as:

1. Exuberant theme that has keywords image cheerful, calm and maturity is match with the
color scale which is being proven by the spread of color in axes scheme. Cheerful has
meaning of kind emotion that bring out happy, excited, jolly and carefree. Calm and
maturity are words that related with maturity which show calmer, more restful, and more
peaceful attitude. This keyword image that tells Exuberant is a fun and happy theme can be
also used for mature people which is identical with colorful and carefree to trying a new
things, which is in the fashion trying to do mix match style look.

2. Neo Medieval theme has heavy and warm as the image keyword. Heavy is related with the
darkness of the colors, which can be seen in the result that Neo Medieval has scoop colors
that very near with hard axes. Even though this themes has story telling about war, which is
symbolized with the dark color, warm as one of the keyword also related. Warm is not only a
word that related with temperature, but also is correlated with red or orange as a warm
colord as symbolized of flame in the war.

3. Svarga theme has many colors in the color image scale result. However, in ITF there is one
word of key colors as Svarga guidance which is warm. This what makes Svarga color image
sacle and key fashion words scheme are positively match.

4. Cortex theme has three key colors which are cool, calm, and pale. These three key colors are
match with image color scale or key fashion words which the scale color is in an area of cool
axes and soft axes. Cool is related with cool or cold temperature, which is symbolized with
blue color. Calm and pale are colors that is near to soft axes because of the mixture with
white color. Calm is word that has image serene, and quite related with cool light blue,
resemble of blue ocean in light day. Cortex theme brings up AI and hi-tech identic with light
colors, which is match with the white mixture and the light of white color.

In the future research, this color image scale analysis should be strengthened with qualitative
method, which consisting a perception of the audiences’ data as well as a possible visual object to
make a semiotic approach, since design is related with language discipline.

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References

1. Dr. Agus Sachari. Metodologi Penelitian Budaya Rupa. Erlangga: Jakarta, Indonesia. 2005. ISBN 979-741-648-
8

2. Dina Midiani; Amalia Sigit; Marisa. Singularity. BEKRAF (Badan Ekonomi Kreatif): Kantor Gedung
Kementerian BUMN Lt.15, Jalan Medan Merdeka Selatan No.13, Gambir Jakarta Pusat, Jakarta 10110
Indonesia. 2019; pp. 7-9, 22-23, 38-39, 48-49, 64-65, 74-75, 90-91, 100-101, 116-117, ISBN 978-602-51050-9-8.

3. Shigenobu Kobayashi. Color Image Scale, 1st ed.; Kodansha, Ltd.: 17-14 Otowa 1-chrome, Bunkyo-ku, Tokyo
112-8652, Japan, 1991; pp. 7-13, ISBN: 4-7700-1564-X.

4. Shigenobu Kobayashi. Colorist, 1st ed.; Kodansha, Ltd.: 17-14 Otowa 1-chrome, Bunkyo-ku, Tokyo 112-
8652, Japan, 1998; pp. 8-15, 104-124, 154-156, ISBN-10: 4-7700-2323-5.

5. Yasraf Amir Piliang. Pendekatan dalam Penelitian Desain. Jurnal Ilmu Desain 2007, Vol. 2 No. 3, pp.125-
134, ISSN 1907-5170

6. Yasraf Amir Piliang. Pluralitas Bahasa Rupa: Membaca Pemikiran Primadi Tabrani. Jurnal Ilmu Desain
2006, Vol. 1 No. 1, pp.63-74, ISSN 1907-5170

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Proceeding Indonesian Textile Conference

(International Conference)
3rd Edition Volume 1 2019

http://itc.stttekstil.ac.id
ISBN : 978-623-91916-0-3

Mathematical Modeling of Size Penetration into the
Yarn in the Warp Sizing

Dalyono Mughni1, Tuasikal M. Amin2

1,2Departement of Texrile Engineering, Faculty of Industrial Technology, Islamic University of
Indonessia, Yogyakarta

Correspondence: 1 [email protected], 2 [email protected]

Abstract: This study discusses the capillary model of the space formed between the fibers in the yarn.
Transfer of solution into yarn is one of the important factors affecting the processes and in application
of textiles. The solution that flows into the space between fibers can cause changes in dimensions
which have a direct effect on the structure of space between fibers in the yarn. Furthermore, in this
study, an analysis of the geometry model of fiber bonds in the yarn suggested by Schwartz was used
to determine the capillary diameter of the yarn and in the determining the area. This study also
considered the nature of fibers which are swelling while absorbing water molecules. The results of the
discussion of this model were used to understand the effects of geometric parameters and materials in
fluid transfer in the application of warp sizing using Poison law. The theoretical calculation resulted
differently compared to the results of research on cotton yarn which are assumed to be that the fibers
are considered perfectly round, uniform twist. The absence of fiber migration greatly affects the
cavity between fibers in the yarn. In addition, other problems were found in the covenant process
such as viscosity stability during the process, as well as the quality of the size recipe used.

Keywords: capillary; geometry; swelling; viscosity

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1. Introduction

The main target of the warp sizing is to produce yarns with high weaving power. This needs to
be supported with the selection of size recipes according to the type of yarn that will be sized to be
made into warp yarns. Furthermore, in order to fulfill the main target, the warp sizing is expected to
coat the yarn not only with the size which is resistant to abrasion, but also with the size which drips
into the yarn, and the amount of size penetration should not be excessive which can cause the warp to
stiffen and lose its flexibility. The viscosity of size solution will affect the coating and penetration of
size into the yarn. Low viscosity will result in more penetrated size in the yarn and the film layer on
the surface of the yarn is not formed.

The size absorption by the yarns in the warp sizing depends on the space between the fibers
present in the yarn, the viscosity of size, the pressure of the roller immersion and the squeezer roller.

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The size absorbed in this case is that which coats (forms film) the surface of the yarn and which is
penetrated (absorbed) into the yarn. Besides, the transfer of size solution into the space between fibers
will cause the fibers in the yarn to swell and this will result in changes in the spatial structure
between fibers. This research focused on the theoretical study of inter-fiber space in yarns related to
absorption in the warp sizing, which was then compared to the results of investigations taking into
account the size that coated the surface of the yarn.

From this description, this research is intended to empirically test the mathematical model of
yarn size absorption in the warp sizing as a weaving preparation process.

2. Estimated Model of Size Absorption in Yarns

2.1. Space between Fibers in Yarn

The space between fibers is ideally a triangular arc, and this space will be filled with liquid. If p is
the circumference formed by the three sides of the fiber cross section which are tangent to each other
as shown in Figure 1, then

p  3 2 r
6

atau

p=r (1)

As shown in Figure 1, the length of each side of the arc which forms the space between fibers is

the same as that 1 of the circumference of a fiber cross section. This can be related to mass per unit
6

length, ,, from fiber (fineness), and fiber density, , then

 =  r2

or

r2   (2)


so equatian (1) becomes

p  (3)


Because it is very small, the space between fibers is considered as a circle so that the diameter of
the space between fibers is,

 p (4)


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r

Figure 1. Inter fiber space in closed packing configuration [1] (5)
The substitution of p with equation (3) is obtained by

1 


or

  (6)
 2

so obtained

  (7)


2.2. Penetration of size into yarn

The sizing begins with wetting. This is not done only on the surface of the yarn, but needs to
penetrate into the yarn through the pores or space between fibers in the yarn.

The relationship between the amount of solution flowing per unit time, V, and the difference in
pressure, P, diameter of space between fibers, , solution viscosity, and the length of inter fiber
space, l, if following Poison law as follows [2], [ 3]:

V  ΔP  2 (8)
8 l

Figure 2. Warp sizing process

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And if Q is a notation of the amount of solution absorbed, and t is a notation from the time the
yarn is submerged in solution, then the relationship mentioned above will become

Q  ΔP  2t (9)
8 l

From equation (4) it can be seen that the penetration of size solution into yarn is proportional to
roller pressure, diameter between fibers and immersion time, and inversely proportional to the
viscosity of the solution.

Next is the substitution of  in equation (9) above with equation (7)

Q  ΔP t   2 (10)
8 l 

then acquired

Q  ΔP t (11)
8   l

Figure 3. Size box

Cotton fibers consist almost entirely of cellulose which has an empirical formula (C6H10O5)n,
where n is the degree of polarity depending on the size of the molecule. In the absorption process,
there is an interaction between water molecules and fiber molecules. all natural fibers have hydroxyl
groups in each of their remaining glucos, and hydrogen bonds can form between these water
molecules and hydroxyl groups.

CH2OH HOH
CH2OH

OO OO CH
CH CH
CH CH CH CH

OH CH 3 H2O

CH O

CH O

OH OH

OH

HOH HOH

Figure 4. Absorption of water molecules by cellulose molecular hydoxyl groups [1]

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Dalyono Mughni : Mathematical Modeling of Size Penetration into the Yarn in the Warp Sizing

The molecular weight of the water is 18 and the molecular weight of the remaining glucose is
162, so if the water molecule joins each hydroxyl group, then the mass of the molecule will increase
by 33%. In fact, not all hydroxyl groups absorb water because of the crystalline region which tends to
remain unchanged. This area is around the area [1], but the fact shows that the absorption of the
water molecules that first joins the hydroxyl group can be followed by the second molecule. In other
words, the water molecules can form layers, the first layer by the first water molecule, and the next
layer by the next water molecule, as shown in Figure 5.

The bond of water molecules is directly formed stronger on the molecular structure, while the
bonds will indirectly be easily released. The arrangement is erratic, but not as free as liquid water
molecules, or ice-shaped water molecules.

The fineness or roughness of a fiber is sometimes determined by its diameter, but in industry or
research, the diameter of a fiber is part of the characteristics of various dimensions. Other sizes such
as cross-sectional areas that indicate the type of fiber to be compared with weight per unit length in a
cross section . The cross-sectional area can be calculated from the density (g/cm3), or its linear density
(g/cm.10-8). Other sizes such as wall thickness are a measurement used to describe cavities from fibers
that are related to the maturity of a fiber. As with yarn numbering, fiber fineness is defined as mass
per unit length, so the size of the fineness of this fiber will determine the amount of fiber in the cross
section of the yarn [5].

When fiber absorbs water molecules, there will be a swelling of fibers, so that the structure of the
yarn will change as shown in Figure 6 [1],

a. Dry b. Wet

c. Direct and indirect absorption 330
Figure 5. Absorption of fiber molecules [1]

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Dalyono Mughni : Mathematical Modeling of Size Penetration into the Yarn in the Warp Sizing

a. Dry fibers b.Wet fibers

Figure 6. Cross section of dry and wet fibers

Thus, equation (3) needs to be revised in accordance with the fiber used for the warp sizing. For
example, if the size is made of cotton fiber as in this study, the size penetration into the yarn is
formulated as equation (11) becomes

Q  1,2 ΔP t (12)
8   l

or

Q  0,15 ΔP t (13)
 l

because the swelling diameter of cotton fiber is 20% [1], so it is multiplied by 1.2.

2.3. The Number space between fibers in the yarn

Equation (5) applies only to one space between fibers in the yarn, whereas the number space
between fibers depends on the number fiber in the yarn, Table 1 shows the relationship between the
number fiber in the yarn and the number space between fibers in the yarn.

Table 1. Total Inter-Fiber Space in yarn close packing structure [1]

Layer Layers Number Number Total
number fiber layer/interfiber interfiber
1
2 11 space space
3 67 0 0
4 12 19 6 6
5 18 37 18 24
6 24 61 30 54
7 30 91 42 96
8 36 127 54
9 42 169 66 150
10 48 217 78 216
54 271 90 294
102 384
486

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2.4. Size filmed on the surface of the yarn

In the warp sizing, size not only seeps or drips into the yarn but also coats the surface of the
yarn. According to Hoshiyama [4] the relationship between the size lining the yarn and penetrating
into the space between fibers depends on the viscosity of the solution, high viscosity will result in
decreased size penetration into the yarn. The relationship can be shown in Figure 7. From the graph
Figure 7 shows that coating the size solution onto the yarn surface tends to increase in proportion to
the increase in viscosity of the size solution, while penetration of the size solution into the yarn tends
to decrease when the viscosity of size solution increases. Thus it can be understood that the thickness
of a size solution will make it difficult to penetrate into the yarn. Therefore, the warp sizing with a
high viscosity of size solution will further strengthen the coating of size on the surface of the yarn.

% ase kanji 150
100
10 100
50 Viskositas (cps) T otal
0
1 P en et rasi

Lapisan

Figure 7. Relationship between viscosity and size percentage [4]

3. Materials and methods

3.1. Material studied

The material studied was cotton yarn with specifications: Ne130 with 22 tpi and 15% uneveness,
Ne140 with 25 tpi and unevenness 14%, Ne150 with 28 tpi and unevenness 13%, then Ne160 with 31 tpi
and unevenness 12%. The size specifications can be seen in Table 2.

Table 2. Specifications for sizing process

Item Ne130 Ne140 Ne150 Ne160

Speed 25 m/menit 25 m/menit 25 m/menit 25 m/menit
Squeez roll
300 kg/cm2 300 kg/cm2 300 kg/cm2 300 kg/cm2
Twist
8,8/cm 10/cm 11/cm 12,4/cm

Warp density 26/cm 34/cm 36/cm 38/cm
Warp Number 3400 4500 4765 4850
100 cps 100 cps 100 cps 100 cps
Viscosity 1% 1% 1% 1%
Draft 14% 14% 14% 14%
Regain

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3.4. Size recipe

Tapioca size is the main ingredient in the size recipe used in this study. The size recipe used in
this study is that every 1 (one) liter of water is 121 grams of size, with details as follow:

 Tapioka: 70 g
 PVA: 30 g
 Solgum: 6 g
 Tylose: 8 g
 Solvilwax: 7 g

3.5. Research procedure

The samples of warp yarn before sizing, for each count, namely Ne130, Ne140, Ne150 and Ne160
were taken from the warp machine, with the change of warp beam of 1 (one) meter, and then
weighed and then divided by the number of yarns.

While samples for warp yarns after sizing, for each count were taken from the sizing machine,
with the weaving beam changes as long as 1 (one) meter, then weighed and then divided by the
number of yarns.

The weight difference per yarn from the results of weighing the sample between warp yarns
before sized with warp yarns after sizing was referred as the size content in the yarn absorbed during
the warp sizing proccess.

4. Results and discussion

The steps to get the theoretical results of the size absorbed in the yarn are to calculate the number
of fiber in the yarn is 15000/(Ne1. ) [5]. Based on the configuration of the close-packing structure
tabulated in Table 1, the interpolation calculation Lagrange interpolation [7] used Microsoft Office
Excel 2007 Software program. in this calculation the number of inter-fiber space was obtained Ne130 =
228, Ne130 = 228, Ne140 = 176, Ne150 = 141, and Ne160 = 114.

The relationship between size coated and penetrated in graph 7 is the relationship between
viscosity and size percentage of Hoshiyama [4], then size penetrated into yarn in this study which
used 100 cps viscosity, was around 35%. Thus for Ne130, the size penetrated is 35% from 14% equal to
4.9%. For Ne140, the size penetrated is 3.76%, while for Ne150, the size penetrated is 3.61% and size
Ne160 penetrated is 3.45 %. The size absorbed from the results of the investigation was obtained as:
Ne130 size absorbed was 14.10% and standard deviation of 1.30%, Ne140 size absorbed was 13.60%
and standard deviation of 0.67%, Ne150 size absorbed was 13.30% and the standard deviation of
0.60% and the size absorbed Ne160 is 13.05% and the standard deviation is 1.30%.

These results showed that the calculation of size penetration into yarn is lower compared to the
results of the investigation. The average calculation result was 2.285% lower than the results of the
investigation or the difference is almost a quarter. Furthermore, it can also be seen that only the yarn
number Ne130 has the same theoretical results compared to the results of the investigation. The
results of theoretical calculations in succession between the numbers Ne130 and Ne140 was 3.27%;

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between numbers Ne140 and Ne150 was 0.42%, between numbers Ne150 and Ne160 was 0.44. The
difference between the results of theoretical calculations shows inconsistency, even though the
fineness of the fibers used and the hardness of yarns or twist factors are the same. The results of
successive investigations between the numbers Ne130 and Ne140 was 0.5%; between numbers Ne140
and Ne150 was 0.3%, between numbers Ne150 and Ne160 was 0.25, the difference between the results
of this investigation also showed inconsistency. Inconsistency or the difference in the value from
theoretical and investigative calculations in this study may be because many factors are considered
ideal. Though the fiber is considered to be perfectly round, uniform twist, lack of fiber migration, all
of which greatly affect the cavity between fibers in the yarn . Besides, the problem is in the covenant
process such as viscosity stability during the process, as well as the quality of the size recipe used.
The results of the regression analysis using Microsoft Office Excel 2007 Software for calculations were
y = -0,00045 x + 0.05955 with r2 = 0.88 while from the investigation, y = -0,000074 x + 0,05098 with r2 =
0,80. When plotted into the result graph as shown in Figure 8.

Size penetrasion 5,00% y = -0,000074 x + 0,05098 Teoritis
4,50% y = -0,00045 x + 0,05955 investigasi
4,00%
3,50% 40 50 60
3,00% Yarn count

30

Figure 8. Linear regression for the results of theoretical calculations and investigation of size
penetration into the yarn

From the regression, the equation obtained with 88% correlation coefficient for theoretical
calculations and 80% correlation coefficients for the results of investigations. Many factors are
considered ideal such as assuming that fiber is considered to be perfectly round [8], uniform twist [2,
1], lack of migration of fibers [1]. This is very affecting the cavity between fibers in the yarn.
Additional problems were found in the covenant process such as viscosity stability [5] during the
process, as well as the quality of the size recipe [8] used, the process, as well as the quality of the
starch recipe [8] used.

5. Conclusions

With the assumptions that the fibers in the cylindrical yarn are perfect, the bonding of fibers in
the cross section of the yarn forms a hexagonal configuration following Schwartz's theory. Uniform
yarn twist, viscosity of the solution during stable condition, fiber swelling due to absorption of water
molecules, drafts that occur in rolling into the warp beam, and regain, the calculation of size
absorption by warp yarn was theoretically lower than the results of the study. The results of
regression analysis of equations obtained with high coefficients do not show the same or parallel
slope. Thus the mathematical model built cannot be used to estimate the size penetrated into the yarn.

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Reference

1. Hearle, J.W.S., et al, 2008, “The Structural Mechanics of Fibres, Yarns, and Fabrics”, Wiley-Interscience,
New York.

2. Goswami, B.C. et al, “Textile Yarn”, 1977, John Wiley & sons, New York.
3. Kundu, P.K., 1990, “Fluid Mechanics”, Academic Press Inc., San Diego.
4. Hosyiyama, T., 1973, “A Practical Guide for the Second Step for Weaving Improvement in Large Scale Units

in Indonesia”, UNIDO, Jakarta.
5. Dalyono, 2005, “Dasar-Dasar Perancangan Produk Tekstil”, Graha Ilmu, Yogyakarta.
6. Morton, W.E., and Hearle, J.W.S., 2008, “Physical Properties of Textile Fibres”, The Tetile Institute

Butterworths, London.
7. Soedojo, R., 1995, “Asas-Asas Matematika, Fisika dan Teknik”, Universitas Gadjah Mada, Yogyakarta.
8. Ivana Gudlin Schwarz and Stana Kovačević, (2016). “A New Pre-Wet Sizing Process - Yes or No?”, Cutting

Edge Research in New Technologies, Prof. Constantin Volosencu (Ed.), ISBN: 978-953-51-0463-

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