series b / 2012 number 1 volume 4 internetworking ... · series b of the internetworking indonesia...

InternetworkingIndonesiaJournal

Volum

e 4N

umber 1

Series B / 2012

ISSN: 1942-9703IIJ ©

2012w

ww

.InternetworkingIndonesia.org

Editors’ Introduction 1

Artificial Intelligence Elman Backpropagation Computing Models 3for Predicting Shelf Life of Processed CheeseSumit Goyal and Gyanendra Kumar Goyal

Analysis of Level and Barriers of E-Commerce Adoption by Indonesian 9Small, Medium and Micro Enterprises (SMMEs)Rajesri Govindaraju and Dissa R. Chandra

Android-Based License Plate Recognition using Pre-trained 15Neural NetworkRizqi K. Romadhon, Muhammad Ilham, Nofan I. Munawar, Sofyan Tan and Rinda Hedwig

Manually Interchangeable Heads of Homemade Computer 19Numerical Control (CNC) MachineJimmy Linggarjati & Rinda Hedwig

The International Journal of ICT and Internet Development

Series B: Computing, Communications, Engineering and Internet Technologies

Internetworking Indonesia JournalThe International Journal of ICT and Internet Development

ISSN: 1942-9703www.InternetworkingIndonesia.org

The Internetworking Indonesia Journal (IIJ) is a peer reviewed international journal devoted to the timely study of Information and Communication Technology (ICT) and Internet development. The journal seeks high-quality manuscripts on the challenges and opportunities presented by information technology and the Internet, and their impact to society. The IIJ is published electronically based on the Open Access Publication Policy, and the journal is indexed by SCOPUS and Google Scholar. Papers in the IIJ appear in one of two series:

Journal mailing address: Internetworking Indonesia Journal, PO Box 397110 MIT Station, Cambridge, MA 02139, USA.

Series B of the Internetworking Indonesia Journal covers the broad technical areas of computing, communications, engineering and Internet Technologies.

Possible topics for papers include, but are not limited to the following:

• Information technology and information systems• Communications technology• Software and hardware engineering• Algorithms and computation• Applications and services• Broadband and telecommunications technologies• Mobile and wireless networks

• Internet infrastructure systems, protocols, standards and technologies

• Multimedia and content development• Education and distant learning• Open source software development and deployment• Cloud Computing, SaaS, and Grid Computing

Series B: Computing, Communications, Engineering & Internet Technologies

• Series A: Community Informatics & Society • Series B: Computing, Communications, Engineering & Internet Technologies

Prof. Edy Tri Baskoro, PhD (ITB, Indonesia)Mark Baugher, MA (Cisco Systems, USA) Lakshminath Dondeti, PhD (Qualcomm, USA)Paul England, PhD (Microsoft Research, USA)Brian Haberman, PhD (Johns Hopkins University, USA)Prof. Svein Knapskog, PhD (NTNU, Norway)

Prof. Bambang Parmanto, PhD (University of Pittsburgh, USA) Prof. Wishnu Prasetya, PhD (Utrecht University, The Netherlands)Graeme Proudler, PhD (HP Laboratories, UK) Prof. Jennifer Seberry, PhD (University of Wollongong, Australia) Prof. Willy Susilo, PhD (University of Wollongong, Australia)Prof. David Taniar, PhD (Monash University, Australia)

Thomas Hardjono, PhD (MIT, USA)

Budi Rahardjo, PhD (ITB, Indonesia)

Kuncoro Wastuwibowo, MSc(PT. Telkom, Indonesia)

International Advisory Board

Series B:Co-Editors

Technical Review Board

Moch Arif Bijaksana, MSc (IT Telkom, Indonesia)Teddy Surya Gunawan, PhD (IIUM, Malaysia)Brian Haberman, PhD (Johns Hopkins University, USA)Dwi Handoko, PhD (BPPT, Indonesia)Martin Gilje Jaatun, PhD (SINTEF, Norway)Mira Kartiwi, PhD (IIUM, Malaysia)Maciej Machulak, PhD (Google, USA)Jesus Molina, PhD (Fujitsu Laboratories of America)

Bobby Nazief, PhD (UI, Indonesia) Anto Satriyo Nugroho, PhD (BPPT, Indonesia) Bernardi Pranggono, PhD (University of Leeds, UK)Bambang Prastowo, PhD (UGM, Indonesia)Bambang Riyanto, PhD (ITB, Indonesia)Andriyan Bayu Suksmono, PhD (ITB, Indonesia)Setiadi Yazid, PhD (UI, Indonesia)

Henri Uranus, PhD(UPH, Indonesia)

Vol. 4/No. 1 B (2012) INTERNETWORKING INDONESIA JOURNAL 1

ISSN: 1942-9703 / © 2012 IIJ

ELCOME to the Spring 2012 issue of the Internetworking Indonesia Journal. This issue sees a number of positive

developments in the Internetworking Indonesia Journal. One of the most significant developments in 2012 is the introduction of a second stream (or series) of the IIJ focusing broadly on ICT and society. As such, going forward the existing IIJ journal will be denoted as Series B and will focus on the broad areas of Computing, Communications, Engineering & Internet Technologies. The new second series (denoted as Series A) will focus on the broad field of Community Informatics & Society. Together with this development is the introduction of an additional co-editor for IIJ Series B, namely Dr Henri Uranus from the Universitas Pelita Harapan (UPH), in Jakarta, Indonesia. The IIJ welcomes his strong leadership and vision.

This regular issue of the journal brings four (4) papers from differing areas of technology. The first paper concerns the use of artificial neural networks to predict the shelf life of processed cheese. Solving this problem is relevant to the dairy industry in India, as the authors are from National Dairy Research Institute, in Karnal, India.

The editors can be reached individually at the following email addresses.

Thomas Hardjono is at [email protected], Budi Rahardjo is at [email protected], Henri Uranus is at [email protected], while Kuncoro Wastuwibowo is at [email protected].

The second paper addresses e-commerce developments in

Indonesia, notably among small, medium and micro Enterprises (SMMEs). The paper seeks to discover the barriers to the adoption of e-commerce among SMEEs in Indonesia, paying particular attention to the non-oil-and-gas processing sector and part of the services sector.

Character recognition is the topic of the third paper. The paper reports on work being done to use open-source Android software on a mobile device to recognize vehicle plate-numbers. The approach is based on the use of artificial neural networks with back-propagation, where training of the neural network is carried out in a desktop computer instead of mobile device to speed up the training. The work has promise to become the basis for low-cost plate number recognition usable for the government sector.

The fourth paper reports on the early development of a low-cost Computer Numerical Control (CNC) machine. Such a machine would be attractive to many small businesses in Indonesia, which so far have had to rely on imported machines. The resulting “home-made” CNC machine allows three different kinds of tools to be manually interchanged.

Thomas Hardjono

Budi Rahardjo Henri Uranus

Kuncoro Wastuwibowo

Editors’ Introduction

W

2 INTERNETWORKING INDONESIA JOURNAL Vol. 4/No. 1 B (2012)

ISSN: 1942-9703 / © 2012 IIJ

Vol.4/No.1 B (2012) INTERNETWORKING INDONESIA JOURNAL 3

ISSN: 1942-9703 / © 2012 IIJ

Abstract— This paper presents the latency of Artificial

Neural Network based Elman models for predicting the shelf life

of processed stored at 30oC. Soluble nitrogen, pH, standard

plate count, yeast & mould count, and spore count were taken as

input parameters, and sensory score as output parameter. Mean

square error, root mean square error, coefficient of

determination and nash - sutcliffo coefficient performance

measures were used for testing prediction potential of the

developed models. In this study, Elman models predicted the

shelf life of processed cheese very close to the experimentally

determined shelf life.

Index Terms— ANN, Shelf Life, Elman, Backpropagation,

Processed Cheese

I. INTRODUCTION

ROCESSED cheese is very nutritious and generally

manufactured from ripened Cheddar cheese, but

sometimes less ripened Cheddar cheese is also added in lesser

proportion. Its manufacturing technique includes addition of

emulsifier, salt, water and sometimes selected spices. The

mixture is heated in jacketed vessel with continuous stirring

in order to get homogeneous paste. This variety of cheese has

several advantages over raw and ripened Cheddar cheese,

such as tastier and longer shelf life. It is a protein rich food,

and is a comparable supplement to meat protein. Neural

Networks have become very famous topic of interest since last

few years and are being implemented in almost every

technological field to solve wide range of problems in an

easier and convenient way. Such a great success of neural

networks has been possible due to their sophisticated nature

as they can be used with ease to model many complicated

functions. In human body, neural networks are the building

blocks of the nervous system which controls and coordinates

the different human activities. Neural network consists of a

Sumit Goyal is Senior Research Fellow at the National Dairy Research

Institute, Karnal, India. He can be contacted at [email protected].

Gyanendra Kumar Goyal is Emeritus Scientist at the National Dairy

Research Institute, Karnal, India.

group of neurons (nerve cells) interconnected with each other

to carry out a specific function. Each neuron or a nerve cell is

constituted by a cell body call cyton and a fiber called axon.

The neurons are interconnected by the fibrous structures

called dendrites by the help of special gapped connections

called synapses. The electric impulses (called Action

Potentials) are used to transmit information from neuron to

neuron throughout the network. Artificial neural networks

(ANNs) have been developed based on the similar working

principle of human neural networks. Artificial Neurons are

similar to their biological counterparts. The input connections

of the artificial neurons are summed up to determine the

strength of their output, which is the result of the sum being

fed into an activation function, the most common being the

Sigmoid Activation Function which gives output varying

between 0 (for low input values) and 1 (for high input

values).

Fig.1. Input and output parameters for Elman backpropagation models

The resultant of this function is then passed as the input to

other neurons through more connections, each of which are

weighted and these weights determine the behavior of the

network. An ANN is devised for specific applications, such as

Artificial Intelligence Elman Backpropagation

Computing Models for Predicting Shelf Life of

Processed Cheese

Sumit Goyal and Gyanendra Kumar Goyal

P Soluble

nitrogen

pH

Standard

plate count

Yeast &

mould

count

Spore

count

Sensory

Score

4 INTERNETWORKING INDONESIA JOURNAL GOYAL & GOYAL

pattern recognition or data classification, through a learning

process [1]. ANNs predicted shelf life of Kalakand [2], milky

white dessert jeweled with pistachio [3], and instant coffee

flavoured sterilized drink [4, 5]. Time-Delay and Linear

Layer ANN models were developed for predicting shelf life of

soft mouth melting milk cakes [6], and soft cakes [7]. Radial

Basis models were successfully applied for predicting shelf

life of brown milk cakes [8]. The objective of this research is

to develop Elman single and multilayer backpropagation

computing models for predicting the shelf life of processed

cheese stored at 30o C.

II. MATERIAL AND METHODS

A. Data Modeling

The data consisted of 36 samples, which were divided into

two subsets, i.e., 30 were used to train and 6 to validate the

Elman backpropagation models. Soluble nitrogen, pH,

standard plate count, yeast & mould count, and spore count

were taken as input variables, and sensory score as output

variable for developing Elman single and multilayer models

(Fig.1).

Several combinations were tried and tested, as there is no

defined rule of getting good results rather than hit and trial

method. As the number of neurons increased, the training

time also increased. Algorithms like Levenberg Marquardt

algorithm, Gradient Descent algorithm with adaptive

learning rate, Bayesian regularization, Powell Beale restarts

conjugate gradient algorithm and BFG quasi-Newton

algorithms were tried. Backpropagation algorithm based on

Bayesian regularization was finally selected for training the

networks, as it gave most promising results. ANN was trained

upto100 epochs with single as well as double hidden layers

and transfer function for hidden layer was tangent sigmoid

while for the output layer, it was pure linear function.

The Neural Network Toolbox under MATLAB software was

used for developing the models. Training pattern of Elman

models is presented in Fig.2.

Fig .2. Training pattern of Elman models

Weights Selection

Error Evaluation

Weights Adjustment

Training Elman models

Training

Dataset

Error Calculation

Minimum

Error

End

No

Yes


ISSN: 1942-9703 / © 2012 IIJ

B. Measures for Prediction Performance

Mean Square Error (MSE) (1), Root Mean Square Error

RMSE (2), Coefficient of Determination: R2 (3), and Nash -

Sutcliffo Coefficient: E2 (4) were used in order to compare the

prediction ability of the developed models.

2

1

expN

cal

n

QQMSE

(1)

2

1 exp

exp1 Ncal

Q

QQ

nRMSE

(2)

2

12

exp

exp2 1N

cal

Q

QQR

(3)

2

1 expexp

exp2 1N

cal

QQ

QQE

(4)

Where,

expQ = Observed value;

calQ = Predicted value;

expQ =Mean predicted value;

n = Number of observations in dataset.

III. RESULTS AND DISCUSSION

Elman model’s performance matrices concerning the single

layer and multilayer models for predicting sensory scores

based on equations 1, 2, 3 and 4 are presented in Table 1 and

Table 2, respectively.

Table 1: Performance of single layer for predicting sensory score

Neurons MSE RMSE R2 E

2

3 7.04669E-05 0.008394454 0.991605546 0.999929533

5 0.000176202 0.013274109 0.986725891 0.999823798

7 0.001911122 0.043716386 0.956283614 0.998088878

9 0.000191461 0.01383696 0.98616304 0.999808539

11 0.000227001 0.015066536 0.984933464 0.999772999

13 0.00022446 0.014981988 0.985018012 0.99977554

15 0.000428985 0.020711958 0.979288042 0.999571015

17 0.001079962 0.03286278 0.96713722 0.998920038

20 0.000437028 0.020905211 0.979094789 0.999562972

25 0.000491983 0.022180685 0.977819315 0.999508017

30 1.21846E-05 0.003490644 0.996509356 0.999987815

Table 2: Performance of multilayer for predicting sensory score

Neurons MSE RMSE R2 E

2

3:3 8.77114E-06 0.002961612 0.997038388 0.999991229

4:4 0.006907863 0.083113554 0.916886446 0.993092137

5:5 2.93847E-05 0.005420764 0.994579236 0.999970615

6:6 0.007876012 0.088746898 0.911253102 0.992123988

7:7 0.000225112 0.015003729 0.984996271 0.999774888

8:8 0.007324755 0.085584785 0.914415215 0.992675245

9:9 0.008067535 0.089819455 0.910180545 0.991932465

10:10 0.000224894 0.014996482 0.985003518 0.999775106

11:11 7.57117E-05 0.008701245 0.991298755 0.999924288

12:12 0.000252731 0.015897527 0.984102473 0.999747269

13:13 2.35759E-05 0.004855497 0.995144503 0.999976424

14:14 0.01241181 0.111408301 0.888591699 0.98758819

15:15 0.000110574 0.010515413 0.989484587 0.999889426

16:16 0.000117277 0.01082945 0.98917055 0.999882723

The developed Elman ANN single and multilayer models

showed that single layer model with 30 neurons performed

the best (MSE: 1.21846E-05, RMSE: 0.003490644, R2:

0.996509356, E2: 0.999987815); while multilayer with 3:3

neurons (MSE: 8.77114E-06, RMSE: 0.002961612, R2:

0.997038388, E2: 0.999991229) performed the best. On

comparing them with each other, it was observed that

multilayer model performed better. Therefore, it was selected

6 INTERNETWORKING INDONESIA JOURNAL GOYAL & GOYAL

for predicting the shelf life. The comparison of Actual

Sensory Score (ASS) and Predicted Sensory Score (PSS) for

single layer and multilayer models are illustrated in Fig.3 and

Fig.4, respectively.

Fig. 3. Comparison of ASS and PSS single layer model

Fig. 4. Comparison of ASS and PSS for multilayer model

Coefficient of determination (R2) was calculated based on the

total variation as explained by sensory scores. Period of

storage (days) for which the processed cheese has been in the

shelf is illustrated in Fig.5.

Fig.5. Sensory score and period of storage for processed cheese

The shelf life is calculated by subtracting the obtained value

of days from experimentally determined shelf life, which was

found to be 29.31 days. The predicted value is quiet close to

the experimentally obtained shelf life of 30 days suggesting

that the developed model is quite effective in detecting the

shelf life of processed cheese.

IV. CONCLUSION

Soluble nitrogen, pH, standard plate count, yeast & mould

count, and spore count were taken as input variables, and

sensory score as output variable for developing Elman models

for predicting the shelf life of processed cheese stored at 30o

C. Mean square error, root mean square error), coefficient of

determination and nash - sutcliffo coefficient were used in

order to compare the prediction ability of the developed

models. Bayesian regularization was selected for training

Elman ANN models. Regression equations were developed

for predicting the shelf life of processed cheese which gave

29.31 days shelf life vis-à-vis 30 days experimentally obtained

shelf life. From the study it is concluded that the developed

Elman models are very efficient for predicting the shelf life of

processed cheese.

REFERENCES

[1] http://www.engineersgarage.com/articles/artificial-neural-networks

(accessed on 11.1.2011).

[2] Sumit Goyal and G.K. Goyal “Advanced computing research on cascade

single and double hidden layers for detecting shelf life of kalakand: an

artificial neural network approach”. International Journal of

Computer Science & Emerging Technologies, vol. 2, no.5, pp. 292- 295,

2011.

http://www.engineersgarage.com/articles/artificial-neural-networks


ISSN: 1942-9703 / © 2012 IIJ

[3] Sumit Goyal and G.K. Goyal “A new scientific approach of intelligent

artificial neural network engineering for predicting shelf life of milky white

dessert jeweled with pistachio”. International Journal of Scientific and

Engineering Research, vol. 2, no.9, pp. 1-4, 2011.

[4] Sumit Goyal and G.K. Goyal “Cascade and feedforward backpropagation

artificial neural networks models for prediction of sensory quality of instant

coffee flavoured sterilized drink”. Canadian Journal on Artificial

Intelligence, Machine Learning and Pattern Recognition, vol. 2, no.6,

pp.78-82, 2011.

[5] Sumit Goyal and G.K. Goyal “Application of artificial neural engineering

and regression models for forecasting shelf life of instant coffee drink”.

International Journal of Computer Science Issues, vol.8, no.4, pp.320-

324, 2011.

[6] Sumit Goyal and G.K. Goyal “Development of intelligent computing

expert system models for shelf life prediction of soft mouth melting milk

cakes”. International Journal of Computer Applications, vol.25, no.9, pp.

41-44, 2011.

[7] Sumit Goyal and G.K. Goyal “Simulated neural network intelligent

computing models for predicting shelf life of soft cakes”. Global Journal

of Computer Science and Technology, vol.11, no.14, Version1.0, pp. 29-

33, 2011.

[8] Sumit Goyal and G.K. Goyal ” Radial basis artificial neural network

computer engineering approach for predicting shelf life of brown milk

cakes decorated with almonds”. International Journal of Latest Trends in

Computing, vol.2, no.3, pp.434-438, 2011.

Sumit Goyal is Senior Research Fellow at National Dairy

Research Institute, Karnal (Haryana) India. He received

Bachelor of Information Technology degree and Masters in

Computer Applications from a central university of

Government of India, New Delhi, India. He received his

Master of Philosophy degree in computer science from

Vinayak Missions University, Tamil Nadu, India. His

research interests have been in the area of artificial neural

networks and prediction of shelf life of food products. His

research has appeared in Canadian Journal on Artificial

Intelligence, Machine Learning and Pattern Recognition,

International Journal of Computer Applications,

International Journal of Computational Intelligence and

Information Security, International Journal of Latest

Trends in Computing, International Journal of Scientific

and Engineering Research, International Journal of

Computer Science Issues, International Journal of

Computer Science & Emerging Technologies, Global

Journal of Computer Science and Technology, amongst

others. He is member of IDA.

Gyanendra Kumar Goyal is Emeritus Scientist at National

Dairy Research Institute, Karnal, India. He received the B.S.

degree and Ph.D. degree from Panjab University,

Chandigarh, India. In the year 1985-86, he did specialization

in Dairy and Food Packaging from School of Packaging,

Michigan State University, USA; and in the year 1999 he

specialized in Education Technology at Cornell University,

New York, USA. His research interests include dairy & food

packaging and shelf life determination of food products. He

has published more than 150 research papers in national and

international journals. His research work has been published

in Int. J. of Food Sci. Technol. and Nutrition, Nutrition and

Food Science, Milchwissenschaft, American Journal of

Food Technology, British Food Journal, Canadian Journal

on Artificial Intelligence, Machine Learning and Pattern

Recognition, International Journal of Computer

Applications, International Journal of Computational

Intelligence and Information Security, International

Journal of Latest Trends in Computing, International

Journal of Scientific and Engineering Research,

International Journal of Computer Science Issues,

International Journal of Computer Science & Emerging

Technologies, Global Journal of Computer Science and

Technology, amongst others. He is life member of IDA and

AFST (I).

8 INTERNETWORKING INDONESIA JOURNAL Vol. 4/No. 1 B (2012)

ISSN: 1942-9703 / © 2012 IIJ


ISSN: 1942-9703 / © 2012 IIJ

Abstract—The contribution of Indonesian small, medium, and

micro enterprises (SMMEs) to the national economy growth has

been proven. Today one form of SMMEs empowerments is the

adoption of e-commerce. Although e-commerce adoption by

SMMEs has many advantages, a number of barriers for the

adoption exist. By understanding the current level and the

barriers of e-commerce adoption by SMMEs, it is expected that

the empowerment of SMMEs can be improved. This paper

reports a study on e-commerce adoption by Indonesian SMMEs

in the non-oil-and-gas processing and the trading, hotels, and

restaurants sectors by using business environment framework.

This study found that most of surveyed Indonesian SMMEs have

strategic plans to adopt higher level of e-commerce. Further, it is

found that human resources, sources of information, and push

forces factors are the significant barriers for e-commerce

adoption by Indonesian SMMEs.

Index Terms—electronic commerce, information technology

adoption, adoption barrier, SMMEs, business environment

framework

I. INTRODUCTION

mall and medium enterprises (SMEs) play a major role in

countries at all levels of economic development [1]. In

addition to the contribution from small and medium

enterprises, Indonesia also recognizes the contribution of

micro enterprises to the growth of the national economy

through employment provision and gross domestic product

(GDP) value which reach 91,03% and 33,08% of national

achievement, respectively [2]. Therefore, the empowerment of

small, medium, and micro enterprises (SMMEs) is one of

Indonesian government’s programs to develop the national

economy [3].

One form of recent SMMEs empowerment is the adoption

of e-commerce. There are some different definitions of e-

commerce from literatures. This study use a definition of e-

commerce as “a process of information exchange and

transaction, which involving products and services, using

Manuscript received June 30, 2012. This work was supported in part by the

Institut Teknologi Bandung under International Research Grant.

Rajesri Govindaraju is with the Faculty of Industrial Technology, Institut

Teknologi Bandung, Bandung, Indonesia (corresponding author; e-mail:

[email protected]).

Dissa R. Chandra is with the Faculty of Industrial Technology, Institut

Teknologi Bandung, Bandung, Indonesia (e-mail: [email protected]).

information technology, such as networks, softwares, non-

wireless electronic equipments, and wireless electronic

equipments” [4].

In order to enhance the e-commerce adoption by Indonesian

SMMEs, Indonesian government has to support the SMMEs.

In this situation, it is crucial for Indonesian government to

understand the current adoption level and the adoption

barriers. Based on the understandings, the government’s

programs for Indonesian SMMEs will be focused on the real

problems effectively.

In the previous studies on e-commerce adoption, Diffusion

of Innovation theory and Technology Acceptance Model

dominated the researches framework [5]. The focus of the

models is the upstream issues of e-commerce, so the detail

aspects in the firms and other external aspects that influence

the firms were not identified. Furthermore, several studies also

used other frameworks, such as external and internal

framework [6, 7] and stakeholder theory [8].

Some of the previous researches, such as [6, 8], studied e-

commerce adoption by Indonesian SMEs. In [6] the barriers of

e-commerce adoption are classified into “too difficult”,

“unsuitable”, and “time/choice” factors. Meanwhile, [8]

identifies “Government”, “Employees”, “Owners”,

“Competitors”, “Customers” and “Managers” as the influential

stakeholders in e-commerce adoption by Indonesian SMEs.

However, in the studies the external barriers of e-commerce

adoption were not identified as detail as the internal barriers.

Therefore, this study aims to give a comprehensive and

detail understanding in e-commerce adoption by Indonesian

SMMEs. Therefore, the questions in this study are: (1) “What

is the current majority level of e-commerce adoption by

SMMEs being surveyed in Indonesia?” and (2) “What are the

barriers of e-commerce adoption by SMMEs in Indonesia?”

II. METHODOLOGY

Many studies identified and used classification models of e-

commerce adoption stages. One of them is a simple and

widely used model in [9]. There is also a model in [10] that is

based on enterprises characteristics clustering. Recently, a

standard model of e-government adoption was released by the

UN/ASPA. Although the model was set up to examine the

stage of e-government adoption, the model also can be used to

assess e-commerce adoption in developing countries [11].

Analysis of Level and Barriers of E-Commerce

Adoption by Indonesian Small, Medium, and

Micro Enterprises (SMMEs)

Rajesri Govindaraju and Dissa R. Chandra

S

10 INTERNETWORKING INDONESIA JOURNAL GOVINDARAJU & CHANDRA

TABLE I

E-COMMERCE ADOPTION STAGE

E-Commerce

Adoption Stage Description

Non-adopter · No website

Level 1 : Presence

[16]

· Use websites (including Facebook, Multiply, Indonetwork domain, etc.) only to display information about products and

services (provide a one-way communication on the website)

Level 2 : Portals

[16]

· Use websites (including Facebook, Multiply, Indonetwork domain, etc.) for two-way communication with customers and

suppliers

· Provide services such as product and customization order, product feedback, and also surveys

Level 3 :

Transaction

integration

[16]


suppliers


· Provide online payment and / or online order fulfilment

Level 4 :

Enterprise

integration

[16]


suppliers


· Provide online payment and / or online order fulfilment

· Integrate internal processes with online booking

· Implement Supplier Relationship Management (SRM) and / or Customer Relationship Management (CRM)

In addition, a model of e-commerce adoption stage is

presented in Table I [12]. By adding a non-adopter stage, the

model was used in this study to categorize the “level of e-

commerce adoption by SMMEs”.

To analyze the barriers of e-commerce adoption, Business

Environment framework [13] is used in this study. The

framework is presented in Figure 1. Business Environment

framework is an enhancement of Marketing Environment

framework [14] that has been widely used as a theoretical base

of marketing studies. Business Environment framework was

chosen because it can provide a comprehensive approach to

analyze the aspects of a company and its business

environment. The theory gives us a way to view a company as

a dynamic system.

Four components of Business Environment framework are

eliminated from the initial model. The elimination process was

done based on its effect to e-commerce business operation by

SMMEs. They are “machine” and “material” component in the

internal environment and “natural” and “demography”

component in the macro external environment.

Based on previous studies [6, 7, 15-22], various variables

influencing e-commerce adoption by SMMEs were identified.

Those variables were grouped into the components of the

Business Environment framework. Selection processes of the

variables were based on the real situation in Indonesia. To get

a better understanding about real situation in Indonesia, a pilot

study were done by involving 3 (three) SMMEs. The latent

and the measured variables that were used in the initial model

are presented in Table II.

Business Environment

Component

Internal

Environment

External

Environment

Material

Management

Machine

Money

Man

Micro

Environment

Macro

Environment

Suppliers

Customers

Market

Intermediaries

Competitor

Public

Economic

Political

Socio-cultural

Technology

Natural

Demography

International

Figure 1. Business Environment Component (Jain, 2009)

The e-commerce adoption barriers for SMMEs influence

their e-commerce adoption. In this regards, to establish a

structural model, we added a latent variable "e-commerce

adoption”. The variable represents the concept of SMMEs’ e-

commerce adoption level which is presented in Table I.

For further data processing, the latent variable “e-commerce

adoption” and its’ measured variable were transformed into

the “obstructed e-commerce adoption” (H) latent variable and

“TA” measured variable.

TA = 4 - "level of e-commerce adoption by SMMEs"

Indonesian government suggests that enterprises grouping

in Indonesia can be done based on nine economic sectors [23].

The sectors with high employment levels, gross domestic

product value, and export values are: (1) trading, hotels, and

restaurants sector, and (2) processing industry sector. In

addition, the processing industry sector is divided into oil-

and-gas industry and non-oil-and-gas industry. Oil-and-gas

industry sector only contains large-scale enterprises, while

non-oil-and-gas sector is an industry sector with a large

number of SMMEs. Therefore, SMMEs in non-oil-and-gas

processing sector and trading, hotels, and restaurants sector

were chosen as the focus of this study. The classification of

SMMEs in this study is based on assets value and gross

income value criteria as stated in [24] and shown in Table III.

A questionnaire set was developed using 4 point Likert like

scaling, from strongly agree until strongly disagree.

Questionnaires were given to 400 SMMEs using email and

direct meeting. Follow-up emails and phone calls were made

to encourage respondents. The response rate was 21.75% with

87 returned questionnaires, but there are only 85 complete

questionnaires that can be used for further analysis.

Respondents are non-oil-and-gas processing, trading, and

restaurants SMMEs in 8 provinces. There is no sample from

hotel business. The majority is the trading business. Majority

of the sample is the micro scale enterprises. The SMMEs’ age

ranged between below 1 year until 61 years and their e-

commerce experience ranged between below 1 year until 7

years. The respondents’ profiles are shown in Table IV.


ISSN: 1942-9703 / © 2012 IIJ

TABLE II

INITIAL LATENT AND MEASURED VARIABLES

TABLE III

CLASSIFICATION OF INDONESIAN SMMES [24]

Enterprises Assets (excluding land

and buildings) Gross Income

Micro ≤ Rp.50,000,000.00 ≤ Rp. 300,000,000.00

Small > Rp. 50,000,000.00

- Rp. 500,000,000.00

> Rp. 300,000,000.00

- Rp. 2,500,000,000.00

Medium > Rp. 500,000,000.00

- Rp. 10,000,000,000.00

> Rp. 2,500,000,000.00

- Rp. 50,000,000,000.00

TABLE IV

RESPONDENTS’ PROFILES

Profile Category % Profile Category %

SMMEs' Age

≤ 1 year 49.4% Desktop

Yes 63.5%

> 1 year - ≤ 5 years 30.6% No 36.5%

> 5 years - ≤ 10 years 8.2% Laptop

Yes 69.4%

> 10 years 11.8% No 30.6%

Economic

Sector

Processing Industry 20.0% LAN

Yes 24.7%

Trading 68.2% No 75.3%

Restaurant 11.8% Internet

Yes 77.7%

SMMEs'

Classification

Micro 76.5% No 22.3%

Small 16.5% Camera

Yes 67.1%

Medium 7.1% No 32.9%

E-Commerce

Adoption

Stage

Non-adopter 30.6% Scanner

Yes 32.9%

Presence 21.2% No 67.1%

Portals 38.8%

Transactions

Integration

8.2%

Enterprises

Integration

1.2%

III. RESULT

Construct validity of each variable was assessed by

calculating the correlation between the value of each variable

and the sum value of each respondent answer using software

SPSS 17.0. Variables with insignificant correlation were

eliminated from the model. Then, reliability was assessed by

calculating the Cronbach alpha coefficient. The calculation

showed that the Cronbach alpha coefficient was above 0.7. In

the next step, we found 7 variables with non-normal

distribution from normality test by calculating z score of

data’s skewness and curtosis. After transforming them using

LISREL 8.7, we eliminated 2 variables. Then, sample size

adequacy and multicolinearity were calculated using KMO

and Bartlett’s Test respectively. The KMO value was 0.780. It

means that the sample size was enough for statistical analysis.

Lastly, based on the value of each variable’s MSA, we had to

eliminate 1 variable. The transformed and eliminated variables

are shown in Table V.

TABLE V

DELETED AND TRANSFORMED VARIABLES

Test Transformed Eliminated

Construct

Validity

PO3

PO4

T2

SE2

Reliability -

Normality

ME2 -

ME4 ME4

ME5 -

P5 -

PU2 PU2

PU3 -

PO2 -

Multicolinearity SE3

Exploratory Factor Analysis (EFA) was conducted to

validate the initial model. There were 10 factors extracted

from 37 measured variables in the EFA’s result. In the initial

model, there were 9 components from 44 measured variables.

The difference of measured variables was caused by the

elimination of 7 measured variables in the data preparation

process.

Although the number of latent variables and factors were

different, there were 2 factors from EFA that were exactly the

same with 2 latent variables defined in the initial model. They

are shown in the Table VI. In addition, high similarity is

indicated by factor 1 with "man" latent variable, factor 2 with

"customers" latent variable, factor 4 with "partners" latent

variable, and factor 5 with "technology" latent variable. It is

shown in the Table VII.

To obtain appropriate names for the factors, generalizations

were done. It could be observed that all of the measured

variables in each factor expressed a general idea. Then, the

general idea was adjusted and established as the factors’ name.

As the result, factor 2 expresses "market" which are products,

customers, and service delivery providers, factor 4 expresses

"internet services" as the SMMEs partners, factor 5 represents

"security" of information technology, factor 7 expresses "push

forces" which is encouragements from the internal and

external environment, factor 8 is "sources of information"

about e-commerce for SMMEs, factor 9 measures supports

and inputs from "enterprises association", and factor 10

associates with the “e-commerce popularity” in general.

Environment Latent Variabel Measured Variabel Code Environment Latent Variabel Measured Variabel Code

Lack of educated human resources M1 Partners have not implemented e-commerce yet P1

Lack of resources with IT skills M2 High cost of internet access P2

Employee resistance M3 Bad service from internet providers P3

Lack of knowledge about the benefits of e-commerce M4 Low internet speed P4

Lack of knowledge about the implementation of e-commerce M5 Lack of application developer P5

Lack of experience using information technology / electronic marketing media M6 Low quality of service delivery P6

Lack of time to learn about e-commerce M7 The absence of the threat from competitors who are using e-commerce PU1

Lack of time to operate e-commerce M8 Lack of support and input from SMMEs associations PU2

Financial investment for the implementation of e-commerce is too high F1 Lack of support and input from import export companies association PU3

Term of return on investment and capital is too long F2 Limited number of other organizations that have used e-commerce PU4

Lack of financial resources F3 Lack of information about e-commerce from media PU5

Insuitability with the characteristics of e-commerce products ME1 Ambiguity of government regulation PO1

Incompatibility of e-commerce with the way organizations conduct business process ME2 Unclear rules of international cooperation in trading PO2

Absence of standards of quality between sellers and buyers ME3 High intensity of changes in government regulations PO3

Lack of support for corporate strategic objectives ME4 Lack of support from the government PO4

Lack of support from management ME5 Limited support from online payment system that is feasible and effective T1

High level of risk decision to use e-commerce ME6 Potential security risks in internet network T2

Incompatibility between customers and e-commerce K1 Information privacy issues T3

Low level of trust from customers about the product quality K2 Low on-line payment security T4

Low level of trust from customers regarding delivery time K3 Low popularity of on-line sales and marketing SE1

Low level of trust from customers about the added-value of buying the products K4 Instability of national economic climate SE2

Limited numbers of customers who use internet K5 Poor condition of transportation infrastructure in Indonesia SE3

Internal

Environment

Micro External

Environment

Partners

Publics

Macro External

Environment

Political

Technology

Socio-economy

Man

Financial

Management

Customers

Micro External

Environment


TABLE VI IDENTICAL FACTORS

TABLE VII

OTHER FACTORS

Furthermore, there were K5 and P1 in the factor 1.

Conceptually, these two measured variables are very different

from the other internal environment measured variables in

factor 1. The sources of P1 and K5 are external factors, which

can be affected, but can not be controlled by the SMMEs.

Therefore, P1 and K5 were taken out from factor 1.

Both, P1 and K5, state the use of internet by customers and

the use of e-commerce by supplier. Internet is the basic

information technology for e-commerce. Furthermore,

customers and suppliers are the elements of SMMEs’ supply

chain. Thus, both of the measured variables represent the use

of information technology in SMMEs supply chain. The new

latent variable for P1 and K5 is called as "supply chain

information technology". The revised model is shown in the

Table VIII.

TABLE VIII

REVISED COMPONENT

The data processing was continued using Partial Least

Square (PLS). PLS was used to calculate each latent variable’s

significance in influencing the overall e-commerce adoption

process by Indonesian SMMEs. The data processing result is

shown in the Figure 2. Three latent variables influence

significantly the “obstructed e-commerce adoption” in

Indonesian SMMEs with T-value above 1.67. They are “Push

Forces”, “Man” and “Sources of Information”.

IV. DISCUSSION

From the data, it is known that 69.4% respondents are e-

commerce adopters. Further, majority of the adopters are at

the second level of e- commerce adoption stage. Although

only a small number of SMMEs adopted e-commerce on a

higher level, but most of the SMMEs have desire to move on

and increase their level of e-commerce adoption. It can be

viewed based on 77.97% of adopter SMMEs and 57.69% of

non-adopter SMMEs who stated their desire to increase their

level of e-commerce adoption in the survey. It reveals that

most of the surveyed SMMEs have low-level of e-commerce

adoption, but they have strategic plans to increase the level.

Based on the result, “Push Forces”, which is reflected by

support from management and threat from competitors, is still

not enough to push e-commerce adoption by Indonesian

SMMEs. For the aspect of competition threat, the result

indicates that there are not many e-commerce adopter

enterprises in Indonesian market. Therefore, SMMEs believe

that there is an absence of threat from e-commerce adopter

enterprises. This result is consistent with the result found in

[8] about the influence of “Owners”, who in general manage

the SMMEs directly, and “Competitors”.

The result also indicates that the quality of Indonesian

SMMEs human resources leads to the low e-commerce

adoption in their companies. It is also consistent with the

result in [8]. This quality is measured by the number of

educated employees, the number of employees with sufficient

Latent

VariabelCode Factor Code

F1 F1

F2 F2

F3 F3

PO1 PO1

PO2 PO2

Initial Model EFA

Financial 3

Political 6

Latent

VariabelCode Factor Code

M1 M1

M2 M2

M3 M3

M4 M4

M5 M5

M6 M6

M7 M7

M8 M8

ME1 ME2

ME2 K5

ME3 P1

ME5 ME1

ME6 ME3

K1 K1

K2 K2

K3 K3

K4 K4

K5 P6

P1 P2

P2 P3

P3 P4

P4 ME6

P5 T1

P6 T3

PU1 T4

PU3 ME5

PU4 PU1

PU5 P5

T1 PU5

T3 9 PU3

T4 PU4

Socio-

economySE1 SE1

Partner

4

5

Publics 7

8

Technology

10

Initial Model EFA

Man

1

Management

2

Consumers

Code Code

M1 ME1

M2 ME3

M3 K1

M4 K2

M5 K3

M6 K4

M7 P6

M8 P5

ME2 PU5

F1Enterprises

AssociationPU3

F2 PU4

F3 SE1

ME5 ME6

PU1 T1

K5 T3

T4

PO1

P2 PO2

P3

P4

Supply Chain

Information

TechologyP1Micro External

Environment

Component

Internal

Environment

Man

Financial

Push Forces

Component

Micro External

Environment

Market

Internet Services

Sources of

Information

Macro External

Environment

E-commerce

Popularity

Security

Political


ISSN: 1942-9703 / © 2012 IIJ

\

Figure 2. SmartPLS Result

IT skills, employees’ resistance, knowledge about the benefit

and implementation of e-commerce, time to learn about e-

commerce, and the organization way of doing business. It

indicates that in Indonesian SMMEs the organization way of

doing business is strongly bounded with the human resources.

Therefore, it is possible that the question about the

organization way of doing business had been translated by

respondents into the human resources way of doing their job.

The other result indicates that Indonesian SMMEs need

more sources of information about e-commerce. The sources

can be e-commerce application vendors, IT consultants and

public information media. In some cases, e-commerce

application vendors or consultants are also direct sources of e-

commerce information.

Eight latent variables have no significant influence on e-

commerce adoption by Indonesian SMMEs. It is good for

further studies to test again the influence of those variables in

a better setting, such as better variable definition, better

operationalization, and better data sample.

This study did not use a proportional sample because we did

not know about e-commerce adoption level proportion of the

population. Until now, it has been no census data about e-

commerce adoption by SMMEs in Indonesia. However, we

realize that we could use SMMEs’ geographic location or

government classification to define the SMMEs groups and

their proportion. Future studies could consider using a

proportional sample.

V. CONCLUSION

From the study, it is found that most of Indonesian SMMEs

participating in the study have strategic plans to adopt higher

level of e-commerce, though majority of the firms currently

adopt e-commerce at the lower level. Furthermore, there are

three significant barriers of e-commerce adoption by

Indonesian SMMEs. They are “Push Force”, “Man”, and

“Sources of Information”.

As “Man” is an internal barrier, it can be solved by the

SMMEs itself and by the help of government. Examples of the

solutions for this problem are recruitment of IT employees,

improvement of employees’ skill by SMMEs, and e-

commerce workshop for SMMEs employees held by

government. In relation to “Push Forces” barrier., to solve the

lack of management support in SMMES, the role of SMMEs

owner as a part of management is very important. Another

aspect of “Push Force” is the threat from enterprises using e-

commerce. The result indicates that e-commerce usage by

other companies has not threatened Indonesian SMMEs yet.

Consequently, it can be expected that government’s effort to

support Indonesian SMMEs to adopt ecommerce which can be

done through socialization, education and training may

increase the adoption rate exponentially.

The “Sources of Information” problems need to be solved

by external entities including government and other

associations outside the SMMEs. As an example, public

information media can be arranged by Indonesian government.

If the number and the quality of these sources are increased,

the barrier of e-commerce adoption in Indonesian SMMEs can

be reduced. In this case the role of government as the initiator

and facilitator is very important.


REFERENCES

[1] M. C. J. Caniëls and H. A. Romijn, "What works, and why, in

business services provision for SME: insights from evolutionary

theory," Managing Service Quality, vol. 15, 2005.

[2] Kementerian Koperasi Dan Usaha Kecil Dan Menengah Republik

Indonesia, "Perkembangan Data Usaha Mikro, Kecil, Menengah

(UMKM) Dan Usaha Besar (UB) Tahun 2005 - 2009," Jakarta,

2010.

[3] Kementerian Koperasi Dan Usaha Kecil Dan Menengah Republik

Indonesia, "Rencana Strategis Kementerian Koperasi Dan Usaha

Kecil Dan Menengah Republik Indonesia Tahun 2010 - 2014,"

Jakarta, 2010.

[4] R. Govindaraju and D. R. Chandra, "E-commerce Adoption

Barrier in Indonesian Micro, Small and Medium Enterprises

(SMEs)," in The International Conference on Industrial

Engineering and Business Management (ICIEBM) 2010

Yogyakarta, Indonesia, 2010.

[5] R. Mohamad and N. A. Ismail, "Electronic Commerce Adoption in

SME: The Trend of Prior Studies," Journal of Internet Banking

and Commerce, vol. 14, 2009.

[6] M. Kartiwi and R. C. MacGregor, "Electronic Commerce

Adoption Barriers in Small to Medium-Sized Enterprises (SMEs)

in Developed and Developing Countries: A Cross-Country

Comparison," Journal of Electronic Commerce in Organizations,

vol. 5, pp. 35-51, 2007.

[7] M. Kapurubandara and R. Lawson, "Barriers to Adopting ICT and

e-commerce with SMEs in Developing Countries: An Exploratory

study in Sri Lanka," in Conference of CollecteR Adelaide, 2006,

pp. 1-13.

[8] R. Govindaraju, D. R. Chandra, and Z. A. Siregar, "Stakeholder

Role in e-Commerce Adoption by Small and Medium Enterprises,"

in IEEE International Conference on Management of Innovation &

Technology Bali, Indonesia, 2012.

[9] R. Kalakota and M. Robinson, E-business 2.0 : Roadmap for

Success. New York: Addisom-Wesley, 2001.

[10] E. Daniel, H. Wilson, and A. Myers, "Adoption of E-Commerce by

SMEs in the UK: Towards a Stage Model," International Small

Business Journal, vol. 20, pp. 253–270, 2002.

[11] J. Toland, "E-commerce in Developing Countries," in

Encyclopedia of Ecommerce, E-government, and Mobile

Commerce Hershey: Idea Group Reference, 2006, pp. 308-313.

[12] S. S. Rao, G. Metts, and C. A. M. Monge, "Electronic Commerce

Development in Small and Medium Sized Enterprises : A Stage

Model and Its Implications," Business Process Management

Journal, vol. 9, pp. 11-32, 2003.

[13] R. Jain, Business Environment. New Delhi: V.K. Enterprises,

2009.

[14] P. Kotler and G. Armstrong, Principles of Marketing. Englewood

Cliffs: Prentice-Hall, 1991.

[15] M. A. Sarlak and A. A. Hastiani, "E-Business Barriers in Iran’s

Free Trade Zones," Journal of Social Sciences, vol. 4, pp. 329-333,

2008.

[16] M. Kartiwi, R. MacGregor, and D. Bunker, "Electronic Commerce

Adoption Barriers in Indonesian Small Medium-sized Enterprises

(SMEs): An Exploratory Study," in Americas Conference on

Information Systems Acapulco, Mexico, 2006.

[17] R. C. MacGregor, L. Vrazalic, S. Carlsson, D. Bunker, and M.

Magnusson, "The impact of business size and business type on

small business investment in electronic commerce: a study of

swedish small businesses," Australian Journal of Information

Systems, vol. 9, pp. 31-39, 2002.

[18] M. A. Sarlak, A. A. Hastiani, L. F. Dehkordi, and A. Ghorbani,

"Investigating on Electronic Commerce Acceptance Barriers in

Dried Fruits Exporting Companies of Iran," World Applied

Sciences Journal, vol. 6, pp. 818-824, 2009.

[19] O. Sorensen and R. Hinson, "E-Business Triggers: Further Insights

into Barriers and Facilitators amongst Ghanaian Non-Traditional

Exporters (NTEs)," in Consumer Behavior, Organizational

Development, and Electronic Commerce: Emerging Issues for

Advancing Modern Socioeconomies Hershey: Information Science

Reference, 2009, pp. 294-310.

[20] M. Stanfield and K. Grant, "Barriers to the take up of electronic

commerce among small-medium sized enterprises," Informing

Science, pp. 737-745, 2003.

[21] L. F. Sugianto and S. Jantavongso, "eBusiness Adoption Studies

Focusing on Thai SMEs," in International Conference on

Computational Intelligence for Modelling Control and Automation

and International Conference on Intelligent Agents,Web

Technologies and Internet Commerce Sydney, 2006.

[22] J. Sutanonpaiboon and A. M. Pearson, "E-Commerce Adoption:

Perceptions of Managers/Owners of Small- and Medium-Sized

Enterprises (SMEs) in Thailand," Journal of Internet Commerce,

vol. 5, pp. 53-82, 2006.

[23] Bagian Data - Biro Perencanaan Kementerian Koperasi Dan Usaha

Kecil Dan Menengah Republik Indonesia, "Statistik Usaha Kecil

dan Menengah Tahun 2007-2008." vol. 2010: DEPKOP, 2009.

[24] "Undang-Undang Republik Indonesia Nomor 20 Tahun 2008

Tentang Usaha Mikro, Kecil dan Menengah," P. R. Indonesia, Ed.,

2008.

Rajesri Govindaraju is an Associate Professor at the Faculty of Industrial

Technology, Institut Teknologi Bandung (ITB), Indonesia. She completed her

bachelor in informatics from ITB and PhD in enterprise information systems

implementation from University of Twente, The Netherlands. Her current

research areas include information systems, ERP, e-business systems

implementation and information system planning.

Dissa R. Chandra is an assistant professor in the Faculty of Industrial

Technology, Institut Teknologi Bandung (ITB), Indonesia. She received a

Bachelor Degree and Master Degree from Industrial Engineering Department

at ITB in 2010 and 2011 respectively. Her research interest is in the area of

Enterprise Information System and information system adoption.


ISSN: 1942-9703 / © 2012 IIJ

Abstract— In this research an automobile license plate

recognizer is developed in a mobile device based on the android

platform to be used as an alternative solution to record road-side

transactions, such as parking and fine. The license plate

recognition is implemented using artificial neural network, where

training of the neural network is carried out in a desktop

computer instead of mobile device to speed up the training using

back propagation algorithm. Result of the training is the artificial

neural network along with their weight is implemented in the

mobile device to recognize characters in license plate. The

training result showed that the neural network is able to

recognize 88.2% characters in sample images outside the training

set. The image processing and neural network implemented in

the mobile device managed to recognize an average of 71%

characters in sample license plate images which are categorized

as having good image quality. Automobile license plate

information is also saved in database inside the mobile device.

Index Terms— android-base, back-propagation algorithm,

license plate recognition, neural network, pre-trained

I. INTRODUCTION

NDROID-based image recognition is one of the

applications that are used in many mobile devices that are

widely available nowadays. As an open source, android is

favorable and is applicable in image recognition as well. In

many parts of Jakarta city, the parking lots are along the street

and monitoring cars come and go is not easy as well as for

security purposes. A mobile device will be a good choice as

one of the media for the parking staff to capture license plate;

later on the image will be recognized and saved in the device.

License plate recognition is widely used in many automatic

parking systems in many countries and widely reported in

series of papers [1-10]. While in this paper we use a simple

back-propagation algorithm for license plate recognition. This

Manuscript received July 27, 2012. This work was supported in part by the

Computer Engineering Laboratory.

R. K. Romadhon was with the Computer Engineering Department, Binus

University, Jakarta, Indonesia (e-mail: rizqi_batavia @yahoo.com).

M. Ilham was with the Computer Engineering Department, Binus

University, Jakarta, Indonesia (e-mail: [email protected]).

N. I. Munawar was with the Computer Engineering Department, Binus

University, Jakarta, Indonesia (e-mail: [email protected]).

S. Tan is with the Computer Engineering Department, Binus University,

Jakarta, Indonesia (phone: +6221-534-5830 ext. 2205; e-mail:

[email protected]).

R. Hedwig is with the Computer Engineering Department, Binus

University, Jakarta, Indonesia (phone: +6221-534-5830 ext. 2205; e-mail:

[email protected]).

simple android-based application will benefit parking staff in

monitoring the cars along the street of Jakarta.

II. LITERATURE REVIEW

License Plate Recognition (LPR) has been actively studied

for couple of decades and still is an active subject of research

because of its wide use and potential application [1]. One of

the most popular approaches for License Plate recognition is

using artificial neural networks (ANN). Various approaches

using neural network can be seen in [1-4]. Variation of the

ANN used for LPR can been in for example in [1-2]. In [1] a

Radial Basis Function Neural Network (RBFNN) is used

instead of the more popular Back-Propagation (BP) neural

network. The evaluation showed better performance over BP

neural network. Other researcher [2] improved the BP learning

algorithm to increase recognition rate. In general there are

many possible algorithms using ANN to recognize characters

in license plate. This shows that the ANN is still a popular

choice to implement LPR. The benefit of using ANN for real-

time applications is because the training and classification

stage can be separated, where the classification time is much

faster than the training time. The classification of image

feature can thus be implemented in mobile devices with low

computing power.

Some researchers have even implemented the LPR

algorithm into Android device, such as in [3-4]. They include

image processing to detect the position of the license plate in

the capture image. Such approach can be time consuming in

low computing power devices such as Android devices. In this

paper the license plate detection problem is omitted simply by

having the user to point the camera directly in front of the

license plate with visual guide in the graphical user interface.

The mobile device then just have to perform light image

processing to remove noise and segmentation of the characters

before feeding them into the feed-forward neural network.

III. PRE-TRAINING THE NEURAL NETWORK

The neural network is trained to recognize a character in a

14x10 pixels-image containing a single character scaled to fill

the whole image area. Training is carried out in a desktop

computer to speed-up the process. There are 4 image samples

for each numeric and upper-case alphabet characters, hence

144 image samples are used as the training set for the neural

network. Each sample image is preprocessed manually by

cropping a character from a larger image, converting to B&W,

and resizing it to 14x10 pixels. Each larger image is a

Android-Based License Plate Recognition using

Pre-trained Neural Network

Rizqi K. Romadhon, Muhammad Ilham, Nofan I. Munawar, Sofyan Tan, and Rinda Hedwig

A

16 INTERNETWORKING INDONESIA JOURNAL ROMADHON ET AL.

photograph of a real automobile license plate. The authors

chose 14x10 pixel input image to minimize the calculation cost

when the neural network is implemented in an android mobile

device.

MATLAB® is used in the desktop computer to preprocess

image samples, construct and train the neural network [11].

After a character image is manually cropped from a larger

license plate image, it is converted from RGB format to

grayscale and then converted to black and white binary data,

where the threshold is calculated using Otsu algorithm [12-

13]. Each sample image is further scaled down to 14 pixels tall

and 10 pixels wide, suitable for the input layer of the neural

network. The whole 144 binary image samples are combined

into a training set.

The architecture of the neural network is a multilayer feed

forward neural network, where two hidden layers are used to

model the character recognition behavior as shown in figure 1.

The input layer is consisted of 140 nodes, each connected to

one pixel of the 14x10 input character images. The output

layer contains 36 nodes, each for one of 36 numerical and

alphabet characters (0 to 9 and A to Z). The first hidden layer

contains 44 nodes, while the second contains 36 nodes. Output

of all nodes in the hidden and output layers are calculated

using the sigmoid activation function (1) to produce

continuous output function with an output range between 0

and 1.

xexf

1

1)(

(1)

After training the neural network using back-propagation

algorithm in MATLAB®, the resulting weights matrix is

exported to be used in the mobile device as a fix weights

matrix implemented in the same neural network architecture,

but without the capability to retrain the weights.

IV. IMPLEMENTATION IN ANDROID-BASED MOBILE DEVICE

Most android device includes camera that can be used to

capture a license plate as a source image, however the source

image must be automatically preprocessed into 14x10-pixels

character images for recognition by the neural network. The

manual cropping used during training is not viable in the

implementation. Therefore a light image preprocessing

algorithm is developed in the mobile device to resize, convert,

and segment the source image into 14x10-pixels character

images.

The first step in the image preprocessing is pre-resizing the

source image into lower resolution to improve performance

and simply cropping out part of the source image that is not a

license plate number. Assuming that the aspect ratio of any

standard license plate is constant, the user can be guided to aim

the camera at a certain distance so that the license plate will

completely fill a certain area in the electronic view finder on

the mobile device. The application can then easily discard all

pixels outside the predefined area and size down the image

into 512x150 pixels for faster processing, while maintaining

most of the image details. Registration date information on the

plate is discarded in this pre-resizing step because of their

lower position in the plate.

Color information is not used in the image processing,

therefore the pre-resized source image is converted to

grayscale and then into black and white binary source image

for faster image processing.

The next step is to segment the binary source image into

multiple images each containing a single character. It is

assumed that all characters in the license plate is separated

from each other, therefore segmentation can be done by

uniquely labeling each island of connected pixels as a single

character. Connected pixels are search in the 8-neighbor of

each pixel as seen in figure 2. Figure 3 shows islands that

contain pixels less than a particular threshold number are

considered as noise and thus neglected. The big islands are

cropped out by finding the left, right, upper, and lower edge

coordinates of the islands in the source image. These cropped

out islands are then scaled and resized to 14x10 character

images.

Input

layer2 Hidden layers

Output

layer

Fig. 1. The 144 input nodes are directly fed from the 14x10 pixels of the

input character image, and then fully connected to the two hidden layers

containing 44 and 36 nodes respectively and finally to the 36 nodes of the

output layer.

N1 N2 N3

N4 N5

N6 N7 N8

Fig 2. The 8 neighbor pixels are searched for any connected pixels with binary

intensity of one.

L2 L5 L5

L5 L5 L5

L5 L5 L6

L3 L3 L5 L5 L6

L3 L3 L5 L5

L3 L5 L5

L5 L5

L5 L5 L5 L5

L4

Fig 3. In a low resolution example, five islands of connected pixels are found

in the above image, where one biggest island (labeled L5) is the character

image, while the other islands are considered as noise.


ISSN: 1942-9703 / © 2012 IIJ

The same neural network architecture as those used in the

pre-training is implemented in the mobile device. Weights

matrix from the pre-training process is imported into the neural

network in the mobile device, resulting in exactly the same

network behavior as the pre-trained neural network. Character

images resulted from image preprocessing in the mobile device

are then fed into the neural network to be recognized as

number or alphabet characters.

For each license plate source image, there will be 3 to 9

characters depending on the plate. These characters are saved

in the local standard SQLite database in the android-based

mobile device. Information saved in the database is type of

automobile (car or motorcycle), license plate characters, and

the time of record. Receipt of the transaction record can be

printed using Bluetooth or Wifi interface to a remote printer.

V. RESULTS AND DISCUSSION

Even though the training result shows that the accuracy is as

high as 88.2%, it is in the contrary with the test result that was

performed in android-based mobile device. The result of image

recognition accuracy that is tested is shown in table 1 where all

license plates that were captured were in good condition. It is

seen that although the same license plate was captured two

times, but the reading result is different from one image to

another. Lightning position as well as camera position played

important role with the image recognition that should be

controlled since the beginning of capturing the image.

In the table 2, we did experiment by capturing the image of

license plate directly from the android-based mobile device

(dual core 1GHz) where some of the images were not well-

captured. It needed 15 to 20 seconds for the device to

recognize each image and it is seen that many of the

recognitions are failed. The image segmentation could not be

performed properly due to the poor images that were manually

captured by mobile device. This poor performance also found

when the license plate was not in the good condition such as

there was smooth crack in between the letters or pointing bolts

under the letters.

After image recognition process was done, the reading then

saved in database and in the form of file.txt format. The

database then is used as parking report which is contained of

type of vehicle, license plate number, and time. The file.txt

will be printed out by using PrintShare application and can be

used as parking receipt for the driver or the owner of the

vehicle.

VI. CONCLUSION

This simple experiment is done in order to build a license

place recognition application running on mobile device with

android operating system, which is an open source operating

system. Even though the result is still far from what is

expected for practical use, this small step can enhance

advanced development of this application in the near future for

road side automobile transaction system, such as parking and

ticketing.

TABLE 1

TEST RESULTS FOR LICENSE PLATE RECOGNITION IN GOOD CONDITION

No Image Capture Recognition

Result

Recognition

Percentage

1

B3289BJH 87.5 %

2

D328SHJH 50 %

3

B8692LH 71.43 %

4

B85926N 71.43 %

05

HS929SIF 75 %

Average: 71%

TABLE 2

TEST RESULTS FOR LICENSE PLATE RECOGNITION WHEN TAKING DIRECTLY

FROM ANDROID-BASED DEVICE

No Image Capture Recognition

Result

Recognition

Percentage

1

J157GPPT 63%

2

H66S7OKI 50%

3

H81S4DCK 38%

4

B6598I 75%

5

B6J08D9 43%

Average: 54%

REFERENCES

[1] Weihua Wang. License Plate Recognition Algorithm Based on Radial

Basis Function Neural Networks. Proceeding of International

Symposium on Intelligent Ubiquitous Computing and Education; 2009,

p. 38-41.

[2] Ning Chen, Li Xing. Research of License Plate Recognition based on

Improved BP Neural Network. Proceeding of International Conference

on Computer Application and System Modeling; 2010, p. V11-482 -

V11-485.

[3] Hsin-Fu Chen, Chang-Yun Chiang, Shih-Jui Yang, Ho, C.C. Android-

Based Patrol Robot Featuring Automatic License Plate Recognition.

18 INTERNETWORKING INDONESIA JOURNAL ROMADHON ET AL.

Proceeding of Computing, Communications and Applications

Conference; 2012, p. 117-122.

[4] Mutholib, A.; Gunawan, T.S.; Kartiwi, M. Design and implementation

of automatic number plate recognition on android platform. Proceeding

of International Conference on Computer and Communication

Engineering; 2012, p. 540-543.

[5] Zheng L., Samali B., He X., and Yang L.T. Accuracy Enhancement for

License Plate Recognition. Proceeding of 10th IEEE International

Conference on Computer and Information Technology (CIT); 2010, p.

511-516.

[6] Kasaei, S.H.M., and Kasaei S.M.M. Extractation and Recognition of the

Vehicle License Plate for Passing Under Outside Environment.

Proceeding of European Intelligence and Security Informatics

Conference; 2011, p. 234-237.

[7] Megalingam R.K., Krishna P., Somarajan P., Pillai V.A., and Hakkim

R.U.I. Extraction of License Plate Region in Automatic License Plate

Recognition. Proceeding of International Conference on Mechanical and

Electrical Technology (ICMET); 2010, p. 496-501.

[8] Feng Z., and Fang K. Research and Implementation of an Improved

License Plate Recognition Algorithm. Proceeding of 4th International

Conference on Biomedical Engineering and Informatics (BMEI); 2011,

p. 2300-2305.

[9] Vesnin E.N. and Tsarev V.A. Segmentation of Images of License Plates.

Pattern Recognition and Image Analysis 16 (2006) 1, pp. 108–110.

[10] Sirithinaphong T. and Chamnongthai K. The Recognition of Car License

Plate for Automatic Parking System. Proceeding of 5th

International

Symposium on Signal Processing and Its Application, Brisbane; 1999, p.

455-457.

[11] Beale M.H., Hagan M.T., and Demuth H.B. Neural Network ToolboxTM

– User’s Guide. The MathWorks, Inc.; 2012.

[12] Greensted A. Otsu Thresholding. Retrieved on Nov 1st, 2011, from The

Lab Book Pages: http://www.labbookpages.co.uk/software/imgProc/otsu

Threshold.html.

[13] Anonymous. Global Image Threshold Using Otsu’s Method – MATLAB.

Retrieved on Nov 1st, 2011, from MathWorks: http://www.mathworks.

com / help/toolbox/images/ref/graythresh.html.


ISSN: 1942-9703 / © 2012 IIJ

Abstract—The aim of this research is to design and produce a

low cost Computer Numerical Control (CNC) machine which is

equipped with interchangeable head for many applications

especially for small-medium creative industry. The CNC machine

has good flexibility where its head can be changed manually and

easily between pen (impact-engraver) mounted and CO2 laser

mounted. The choice will depend on the application that is chosen

by the user. The software that is used is basically from open

source software and its precision is “what you see is what you

get” (WYSIWYG). Some creative applications are successfully

performed as shown in this paper, and its lower-production cost

is favorable for small-medium industry. By the year 2014, this

machine will be produced in a mass production and will be

marketable in Indonesia.

Index Terms—CNC machine, interchangeable head, pen head,

impact-engraver, CO2 laser head, creative industry applications

I. INTRODUCTION

ince the first CNC patent was published in 1995[1], many

industries start to use this machine for many applications especially for shaping process which is performed on a work

piece. This patent is also referenced by more than 100 patents

that produce similar machine but for special purposes. When it

was first developed, specialized hardware including

application specific integrated circuits were required for any

practical machines. With the performance of modern Personal

Computers (PCs), it is entirely practical to use a standard PC

to make a useful machine. In fact the majority of commercial

controllers are now simply ruggedized PC architectures which

include some form of isolated high speed digital inputs and

outputs[2]. A laser-based CNC machine from China that is sold in

Indonesia has price range from US$10,000 up to US$15,000

depending on the dimension and purposes. Nevertheless,

usually this typical CNC machine is only for one mounting

device since each application will need one head either for

cutting or welding as well as for drilling or engraving. We

studied that one company in India offered different kind of

machine for different applications such as laser cutting, laser

marking, laser engraver, laser 3D subsurface, and laser

This work is partially supported by “Hibah Bersaing KOPERTIS Wilayah

III” under Grant number 102/DRIC/IV/2012.

J. Linggarjati and R. Hedwig are with the Computer Engineering

Department, Bina Nusantara University, Jakarta, Indonesia (phone: +6221-

534-5830 ext. 2144; e-mail: [email protected] and [email protected],

respectively).

welding machine. It means that no single CNC machine can

do the several tasks simultaneously.

Understanding the needs for lower cost CNC machine as

well as multi purposes function to be used in small-medium

enterprise in Indonesia, we did a small research about these

needs, especially for creative industry. It took one year to

complete this homemade CNC machine construction with tool

path feedback control[3], which is equipped with one servo

motors and two stepper motors. Since the first design was for

writing purpose, an inked-pen head was mounted and the

machine was functioned as if it was a plotter. The drawing

result on a paperboard can be seen in figure 1(a). Having

succeeded with the first step, we then mounted an impact-

engraver head and used it for metal which was successful as

well and the result can be seen in figure 1(b). Since the

machine was developed in Computer Engineering Laboratory

at Bina Nusantara University, the main purpose of this

machine was for Printed Circuit Board (PCB) drawing,

drilling or technical drawing. In year 2012, we got a research

grant that supported the continuum of this research which

enhanced the CNC machine design by manually mounting it

with CO2 laser head. Actually CO2 laser can be used to cut

metal target[4], however the energy of the laser for this

machine is only 40Watt, and the application merely only for

paper, plastic, wood, and acrylic but it could cut, mark, and

engrave with WYSIWYG precision.

(a) (b)

Fig. 1 Engraving result on (a) paperboard target using inked-pen with

rectangle 10x10mm2 and (b) metal target, i.e. iron, using impact-engraver-pen.

II. DESIGN AND EXPERIMENTAL SETUP

The homemade CNC machine’s dimension is 800mm x

700mm x 1200mm as shown in figure 2 and the block diagram

can be seen in figure 3 with its working area’s dimension is

500mm x 450mm. It has three axes (X, Y, and Z) with two

racks to put the CPU inside. Most of previous CNC machine is

using table moving [5,6], while for this design the table is not

Manually Interchangeable Heads of Homemade

Computer Numerical Control (CNC) Machine

Jimmy Linggarjati and Rinda Hedwig

Computer Engineering Department, Bina Nusantara University, Jakarta, Indonesia

S

20 INTERNETWORKING INDONESIA JOURNAL LINGGARJATI & HEDWIG

ISSN: 1942-9703 / © 2012 IIJ

moving to the axes (X and Y), instead the two axes are driven

by a DC servo motors (TAMAgawa 200W) and a stepper

motor (VEXTA ASM 66A – 1.7A – 100W), respectively,

which in turn drive two precision ball-bearing screws, in order

to provide highly accurate movements. The end-tool, which

holds the pen, is placed along the Z axis. It is also driven by a

stepper motor (VEXTA ASD 13AA – 0.9A), equipped also

with a precision ball-bearing screw. Nevertheless, during the

CO2 laser operation the Z axis was fixed, since the focus

length of the laser is determined only by the focus-lens.

Fig. 2 Manually interchangeable cutting head of homemade CNC.

Fig. 3 Block diagram of the CNC system.

Four (4) wheels are attached to the four-legs of the CNC

machine so that we can move the position of the entire system

smoothly from one place to another. There is a 20Watt water

circulation pump for water cooling system during CO2 laser

operation, as well as vacuum filtration system with its energy

consumption as high as 750Watt. The total power

consumption for CO2 laser operation can vary up to 1500Watt.

This energy consumption can vary depends on the technology

that we choose. We can reduce the energy consumption by

changing the cathode ray tube (CRT) to liquid crystal display

(LCD) monitor. During the pen head operation, the vacuum

filtration system and the water circulation pump are not used.

The operating system that is used to control is based on Linux

Ubuntu 10.04 with open source program such as Linux

CNC[7-10] and Inkscape[11].

A comprehensive study[12] was done in order to select

appropriate laser for building a low-cost CNC machine and we

finally chose CO2 laser for its lower cost per watt along with

good beam quality as well as its wide range of average output

power ranging from a few watts up to over 60kW. The CO2

laser that is used is a continuum type with wavelength of

10.6m (power 40Watt; triggering voltage 20kV; current 16-

18mA; length 700mm; 50mm; water cooling system;

lifetime 1500-1800 hours). The setup of this system can be

seen in figure 4 where three sets of 20mm gold plated mirror

and a 18mm ZnSe lens (f = 50.88mm) are attached to the tools

of X, Y and Z axes. Figure 5 shows the Z axis which mounted

with impact-engraver pen, and “mirror-lens” for CO2 laser.

For reminder, the Z axis was fixed during “mirror-lens”

mounting and the distance between working area and the lens

are accordingly to the lens focal length, i.e. 50.88mm.

The materials that were used for this experiment are

plywood, acrylic, and cardboard, PCB with maximum 5mm,

3mm, 5mm, and 1mm thickness, respectively. The total

production cost that is needed to construct the whole system

was US$6,250 (US$1 equal to 9,600 rupiahs).

40

Wa

tt C

O2 L

ase

r H

ea

d

36mm

moves along X axes

mo

ve

s a

lon

g Y

axe

s

f = 50.88mm

working area of 500mm x 450mm

mirror

mirror mirror

Fig. 4 Setup of CO2 laser based system.

III. APPLICATIONS AND DISCUSSION

At the beginning, the whole system was dedicated for PCB

production where a piece of paper was set on the working area

and the drawing was done accurately with an inked-pen – only

used as a prove test that the precision of this machine is

enough for PCB engraving application. Later on the PCB was

drilled by the use of automated hand drill that was controlled

with computer. For further application, a CO2 laser head was

mounted in the CNC and functioned to cut cardboard,

plywood, and acrylic. The alignment for laser mounted head

was no easy at the beginning, we were using HeNe laser as

guidance along with a white thin string. The alignment took

several days before finally we fixed the position of the mirrors

and lens. Once this alignment had successfully done, the

manual interchange among pen head, spindle head, and laser

This area is interchangeable


ISSN: 1942-9703 / © 2012 IIJ

head was easy. We just replaced the Z axis manually and once

we changed the head back to laser, we just had to do minor

adjustment with the guidance of laser beam on the paper.

Several results are shown in figure 6, such as the traditional

shadow puppet made of plywood, the picture engraved on

acrylic, and the 3D object made of plastic board.

Fig. 5 The head was mounted along the Z axis. From left to right: impact-

engraver-pen and mirror-lens.

(a) (b)

(c)

Fig. 6 Some results produced by the use of CO2 laser based CNC machine,

they are (a) traditional shadow puppet made of plywood, (b) cat’s picture

engraved on the acrylic, and (c) 3D object made of plastic board.

The accuracy of material produced compared to what was

displayed in the monitor was precisely the same and with

100% reproducibility as shown in figure 1a (using inked-pen)

and figure 7 (using laser-cut). The time consumption needed

for each process was depended on the size of the object and its

thickness. The thicker the object, the slower the laser will

move. The time consumption for 350mm x 250mm plywood

shadow puppet with lots of detailed, will take around 2 hours.

As for the engraved cat picture on the 105mm x 130mm

acrylic and the 3D dragon object made of plastic board will

take 3 hours and 1 hour, respectively.

When we tried to draw an electronic circuit on the PCB by

using the CO2 laser, we had to apply paint stripping

technique[13-15]. It has been studied previously that the

threshold of copper for the plasma generation in TEA CO2

laser was considerably high because of its high reflectivity

(more than 95%) at around 10.6m[16]. The process of paint

stripping as shown in figure 8 is explained as follows, the

paint on top of PCB will absorb the CO2 laser ablation since

PCB (made of copper) reflects most of the laser energy

leaving a clean stripped surface. Due to most metal’s high

reflection factor, metallic surfaces are suitable for laser

cleaning. Typically, the substrate is not mechanically or

thermally stressed or stained by the CO2 laser stripping

process. The process converts the stripped paint to particles

and vapor which are automatically removed by a vacuum

filtration system.

Fig. 7 Reproducibility and accuracy of 10x10mm2 squares using laser cut. We

got WYSIWYG result.

We tried to apply several kinds of paints in order to get the

most effective PCB that would be successfully etched. Even

though the paint stripping process finally was successful, we

failed to get a good etched-PCB during the etching process for

several times until we found that varnish is the most effective

paint to be layered on top of PCB before paint stripping

process as shown in figure 9. Nevertheless, we did not realize

that when the CO2 laser attacked the opened surface of the

PCB (the paint already removed from the surface on the first

bombardment) for the second time, the laser beam was

MIRROR

LENS

22 INTERNETWORKING INDONESIA JOURNAL LINGGARJATI & HEDWIG

ISSN: 1942-9703 / © 2012 IIJ

reflected back to the laser head and it removed the reflective

layer of the back mirror of the laser head. It happened since

we programmed the laser to do paint stripping twice for thick

path and once for thin path. Therefore, the laser head was

failed to produce 40Watt energy but produce two separated

beams instead, with a very low energy that could not even cut

a 5mm plastic board.

PCB

reflecting

absorbingthickness

paint layer

target plasma

laser beam

Fig. 8 Laser stripping process.

Fig 9 Example of the etched PCB after paint stripping process.

This result also proved that when the distances of laser

pathway closer to the laser head, the laser beam reflection

would come back to its path and attack the laser head. This is

also due to copper’s high reflectivity for laser wavelength at

10.6m. Therefore we need to put anti back reflection tool just

in front of the laser head.

This machine had been demonstrated to other Departments

(such as Architecture Department) and they showed their

interest to utilize this CNC machine for any design purposes.

In the mid of December 2012, we already offered this machine

to a small homemade CNC industry which also showed his

great interest in the machine. The further collaboration will be

discussed in order to make this machine marketable in 2014.

However, we still want to do further development research in

3D cutting or sculpturing or engraving on the woods.

IV. CONCLUSION

Building a home-made CNC machine with a low cost

budget gives advantage for small-medium industry, especially

when this CNC machine can be manually interchanged with 3

different kinds of tools according to its needs. This CNC

machine can be attached with pen head, spindle head, and

laser head for different purposes but the most useful part

happened when we attach with laser head. The open source

program also support this machine to perform with 100%

reproducibility and WYSIWYG accuracy.

REFERENCES

[1] M.R. Wright, D.E. Platts, D.B. French, G. Traicoff, M.A. Dupont, and

G.A. Head,“CNC Control System Patents,” US patent 545393, Sep 26,

1995.

[2] McKilliam G., “CNC Overview White Paper,” Industrial Automation

Series Ltd., Oct 2012.

[3] Gong Zhiming, Huang Sheng, ZengHaoand Chen Xiaoqi, “CNC Laser

Marking on Freeform Surfaces without Prior Geometrical Information,”

in Proc. 2002 IEEE/RSJ Intl. Conf. Intelligent Robots and Systems,

Lausanne - Switzerland, 2002, pp. 1862-1867.

[4] J. Wang, “An Experimental Analysis and Optimisation of the CO2 Laser

Cutting Process for Metallic Coated Sheet Steels,” Int. J. Adv. Manuf.

Technol., vol. 16, pp. 334-340, 2000.

[5] A. Hace, K. Jezernik, B. Curk, and M. Terbuc, “Robust Motion Control

of XY Table for Laser Cutting Machine,” in Proc. 24th

Annu. Conf. IEEE

Industrial Electronics Society, vol. 2, 31 Aug-4 Sept 1998, pp. 1097-

1102.

[6] Cao Shukun, Zhang Heng, ZeXiangbo, Yang Qiujuan, and Ai

Changsheng, “Software and Hardware Platform Design for Open-CNC

System,” in IEEE Int. Symposium on Knowledge Acquisition and

Modeling Workshop, 21-22 Dec 2008, pp. 139-142.

[7] Wang Changlei, Cao Shukun, Zhang Qianqian, Sui Zhiming, and Yang

Shangwei, “Design of Five-Axis CNC Based on Linux,” in Int. Conf.

Computer Science and Network Technology, vol. 2, pp. 1336-1339, 24-

26 Dec 2011

[8] Huang Hai-peng, Chi Guan-xin, and Wang Zhen-long, “Development of

a CNC System for Multi-Axis EDM Based on RT-Linux,” in World

Cong. Software Engineering, vol. 3, pp. 211-216, 19-21 May 2009.

[9] Wang Zi-niu, Li Song, and Wang Yan, “Researching of Real-Time

Version and Testing Its Performance of CNC System Based on RT-

Linux,” in Int. Conf. Networking and Digital Society, vol. 2, pp. 174-

177, 30-31 May 2009.

[10] TianrongGao, Dong Yu, DongfengYue, and Yi Hu, “Design and

Implementation of Communication Platform in CNC System,” in Int.

Conf. Mechatronics and Embedded Systems and Applications, pp. 355-

360, 15-17 July 2010.

[11] D. Spinellis, “Drawing Tools,” IEEE Software, vol. 25, issue , pp. 12-

13, May-June 2009.

[12] A.J. DeMaria, and T.V. Hennessey, Jr., “The CO2 Laser: The Workhorse

of the Laser Material Processing Industry,” SPIE Professional

Magazine, submitted for publication.

[13] R.J. Ranalli, “Laser-Based System and Method for Stripping Coatings

from Substrates,” US patent: 5662762, Sep 2, 1997.

[14] G. Schweizer, and L. Werner, “Industrial 2-kW TEA CO2 Laser for

Paint Stripping of Aircraft,” in Proc. 10th Int. Sym. Gas Flow and

Chemical Lasers, vol. 2502, pp. 57-818, March 31, 1995.

[15] Sp.G. Pantelakis, Th.B. Kermanidis, and G.N. Haidemenopoulos,

“Mechanical Behavior of 2024 Al Alloy Specimen Subjected to Paint

Stripping by Laser Radiation and Plasma Etching,” Theo. and Appl.

Fracture Mech., vol. 25, issue 2, pp. 139-146, Sept-Oct 1996.

[16] MM. Suliyanti, R. Hedwig, H. Kurniawan, and K. Kagawa, “The Role

of Sub-Target in the Transversely Excited Atmospheric Pressure CO2

Laser-Induced Shock-Wave Plasma,” Jpn. J. Appl. Phys., vol. 37, pp.

6628-6632, 1998.

Internetworking Indonesia JournalThe International Journal of ICT and Internet Development

ISSN: 1942-9703www.InternetworkingIndonesia.org

The Internetworking Indonesia Journal (IIJ) is a peer reviewed international journal devoted to the timely study of Information and Communication Technology (ICT) and Internet development. The journal seeks high-quality manuscripts on the challenges and opportunities presented by information technology and the Internet, and their impact to society. The IIJ is published electronically based on the Open Access Publication Policy, and the journal is indexed by SCOPUS and Google Scholar. Papers in the IIJ appear in one of two series:

Journal mailing address: Internetworking Indonesia Journal, PO Box 397110 MIT Station, Cambridge, MA 02139, USA.

Open Access Publication Policy: The Internetworking Indonesia Journal (IIJ) provides open access to all of its content on the prin-ciple that making research freely available to the public supports a greater global exchange of knowledge. This follows the philoso-phy of the Open Journal Systems (see the Public Knowledge Project at pkp.sfu.ca). The IIJ issues are published electronically (PDF) and there are no subscription fees. Such access is associated with increased readership and increased citation of an author’s work.

No publication fees: There is no publication fees to authors for publishing in the IIJ. This is consistent with the open access phi-losophy adopted by the IIJ.

Copyright Policy: The IIJ adopts the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) license. The IIJ is an academic journal dedicated to the open exchange of information. For this reason, the IIJ is freely available to individuals and insti-tutions. Copies of this journal or articles in this journal may be distributed for research or educational purposes free of charge and without permission. However, commercial use of the IIJ website or the articles contained herein is expressly prohibited without the written consent of the editor.

Authors who publish in the IIJ will release their articles to the IIJ under the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) license. This license allows anyone to copy and distribute the article for non-commercial purposes provided that appropriate attribution is given. For details of the rights authors grants users of their work, see the “human-readable summary” of the license, with a link to the full license:

• Creative Commons licenses: http://creativecommons.org/licenses/• Human-readable summary: http://creativecommons.org/licenses/by-nc-nd/2.5/• Full license: http://creativecommons.org/licenses/by-nc-nd/2.5/legalcode

Publication Policies

• Series A: Community Informatics & Society • Series B: Computing, Communications, Engineering & Internet Technologies

Manuscript Submission Guidelines

Language: The primary language of the Internetworking Indonesia Journal is English due to its international readership. Note that the IIJ does not accept requests for the translation of submitted manuscripts.

Format: Papers should be submitted using the IIJ Template document for MS-Word. The IIJ format is derived from the popular IEEE 2-column text format. The IIJ template can be found on the IIJ website.

Length of manuscript: Papers submitted to the IIJ should not exceed 7 pages of the IIJ 2-column format.

Originality: Papers submitted to the IIJ must not have been previously published or committed to another publisher under a copyright transfer agreement, and must not be under consideration by another journal. Papers previously published at conferences can be submitted to the IIJ, but must be revised so that it has significant differences from the conference version.

Ethics Statement: Authors wishing to submit papers to the IIJ are requested to read the IIJ Publication Ethics Statement, which can be found on the IIJ website.

Internetworking Indonesia JournalISSN: 1942-9703

www.InternetworkingIndonesia.org

series b / 2012 number 1 volume 4 internetworking ... · series b of the internetworking indonesia...

Documents