Exploration of an Indonesian Currency Legality Detection System by

Utilizing Image Intensity of RGB Mean Values

Ratnadewi Ratnadewi

, Aan Darmawan Hangkawidjaja

, Agus Prijono

Rudy Wawolumaja

, Kartika Suhada

, Maria Christine Sutandi

Andrew Sebastian Lehman

, Elty Sarvia

and Kervin Lusiano

Department of Electrical Engineering, Maranatha Christian University, Jl. Surya Sumantri 65, Bandung, Indonesia

Department of Industrial Engineering, Maranatha Christian University, Jl. Surya Sumantri 65, Bandung, Indonesia

Department of Civil Engineering, Maranatha Christian University, Jl. Surya Sumantri 65, Bandung, Indonesia

Department of Computer Engineering, Maranatha Christian University, Jl. Surya Sumantri 65, Bandung, Indonesia

rudy.wawolumaja@eng.maranatha.edu, kartika.suhada@eng.maranatha.edu, maria.cs@eng.maranatha.edu,

andrew.sl@eng.maranatha.edu, elty.sarvia@eng.maranatha.edu, kervinlusiano17@yahoo.com

Keywords: Naïve Bayes, Entropy, Contrast, Correlation, Energy, Homogeneity, RGB.

Abstract: Money is one of the objects used by the public to carry out legal buying and selling transactions in a country.

The problem is there are fake bills that are printed by irresponsible people, so everyone needs to be able to

know that the banknotes received is fake or genuine. But not everyone can detect the authenticity of a

banknote, so a tool is needed to detect the banknote is genuine or fake. In this paper, software has been

designed to detect the authenticity of Indonesian currency. In this paper, feature extraction of the grey level

co-occurrence matrix with the features of entropy, contrast, correlation, energy and homogeneity is used to

detect the nominal value of Indonesian banknotes and to detect the validity of Indonesian banknotes; the

extraction of red-green-blue features with features is used mean R, mean G and mean B. The detected

Indonesian currency was Indonesian currency from 2004-2016, with nominal values of Rp. 1000, Rp. 2000,

Rp.5000, Rp. 10000, Rp. 20000, Rp. 50000, and Rp.100000. The classification process uses Naïve Bayes.

From the test results, the system works well for reading the nominal value of Indonesian banknotes and

detection the validity of the money can function properly.

1 INTRODUCTION

The rapid development of science and technology

encourages humans to create tools that can simplify

human work, one of which is a tool to read the

nominal value of money and detect the authenticity of

banknotes. After using this tool then humans will

more easier to save and withdraw money from an

automatic cash withdrawal deposit machine or also

https://orcid.org/0000-0001-6487-8101

https://orcid.org/0000-0002-6474-8013

https://orcid.org/0000-0001-6287-8781

https://orcid.org/0000-0003-3975-4830

https://orcid.org/0000-0002-8126-300X

https://orcid.org/0000-0002-8352-5675

https://orcid.org/0000-0002-7311-1209

https://orcid.org/0000-0003-3708-8723

https://orcid.org/0000-0002-4906-3227

known as an Automatic Teller Machine (ATM) and

there are also several machines that deal with money,

for example vending machines that sell tickets trains,

or sell drinks and others.

Some of the research that have been carried out

will be presented here: detecting the authenticity of a

currency through digital image processing, the Bit

Plane Slicing technique is used to extract the most

significant features and the application of the Canny

Ratnadewi, R., Hangkawidjaja, A., Prijono, A., Wawolumaja, R., Suhada, K., Sutandi, M., Lehman, A., Sarvia, E. and Lusiano, K.

Exploration of an Indonesian Currency Legality Detection System by Utilizing Image Intensity of RGB Mean Values.

DOI: 10.5220/0010743700003113

In Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science (ICE-TES 2021), pages 9-17

ISBN: 978-989-758-601-9

edge detection algorithm is also used. The image of

banknotes with 256 grey-levels is decomposed into 8

binary images. Images that have a higher bit order

rate are evaluated for image grayscale banknotes by

applying the Canny edge detection algorithm. Then

the results are compared between real and counterfeit

banknotes using the same detection technique. From

the observation, it was found that the results of edge

detection using an image that was sliced by bit-plane

results were more accurate and could detect it faster

than directly processing the original image without

being sliced. The limitation problem in this study is

that detection is only for Kuwait banknotes based on

the features of the money and comparisons are made

between real money and counterfeit money based on

component connectivity features, average value,

standard deviation and SNR. The further research

suggested in this paper is carried out by expanding

the scope of observations to colour images using six

bit-planes to verify that will be produced more detail

from grayscale bit-planes (Alshayeji et al., 2015).

In this paper discusses the detection of counterfeit

banknotes using ordinary light rays, the observed

attributes of watermarks and recto verso and currency

ornaments. The image of banknotes is carried out by

the process of converting a colour image into an

image with a grey level, the process of edge detection,

feature extraction, matching of results against

predetermined areas. In this paper, there are no

quantitative or qualitative observations (Giri, 2019).

In this article explains the detection Indian

banknotes using digital image processing techniques.

There are six characteristics of Indian banknotes

chosen to detect counterfeiting, including:

identification mark, security thread, watermark,

numeric watermark, floral design and micro lettering.

Extraction of characteristic features is carried out on

the image and compared with the characteristic

features of the original banknotes. Decision making

is done by counting black pixels. In this article

explain to design a low cost system and a fast decision

making system. The proposed method is inspired by

the analysis of hidden marks on the image of

banknotes. The image of the banknote is obtained

through the camera by applying a white backlight to

the banknote, so that a hidden currency sign appears

in the image. The image is further processed by

applying image processing techniques, such as: image

pre-processing, edge detection, image segmentation,

characteristic extraction. The feature extraction

process can be extended up to 100 Rupees. Six

features are extracted within 1 second. The complete

methodology was carried out for Indian banknotes of

20,50,100, 500 and 1000. The method is very simple

and easy to apply. If the hardware is designed for

image acquisition it helps to minimize the currency

counterfeiting problem. This technique is used to

extract six characteristics of banknotes which include

identification marks, security threads, floral designs,

numeric watermarks, and watermarks (Pambudi et al.,

2016), and the micro letter on the security thread. The

system also extracts hidden features, namely the

latent images of banknotes. The proposed work is an

approach to the extraction of the characteristics of

Indian banknotes. The serial number can also be

extracted using a latent image extraction procedure.

The system can extract features even though the test

image size is different when compared to the

reference image (Prasanthi & Setty, 2015).

The circulation of counterfeit money in Indonesia

at this time may not have invisible ink. Invisible ink

is a security feature for banknotes, and money

counterfeiters do not have the ability to counterfeit

invisible ink in Indonesia. The banknotes genuine or

fake are determined by identifying the presence of

invisible ink. This research developed a software to

determine the nominal value of a banknotes and its

authenticity through one of the banknotes safeguards

features, namely invisible ink.

This software uses Digital Image Processing

technology as an authentication process and Artificial

Neural Networks more specifically Learning Vector

Quantization neural networks (Indradewi &

Ariantini, 2018) as an identification process. In the

authentication process, several processes are carried

out, namely the segmentation process that uses the

green histogram threshold value, the area calculation

process using the chain-code method, and the area

filter process, while the process of identifying the

nominal Indonesian currency (IDR) is carried out by

the feature extraction process with Discrete Fourier

Transform (TFD) and LVQ neural network. The trial

results showed that the average percentage of success

at the authentication stage was 98.77% and the

average percentage at the identification stage of the

Indonesian currency (IDR) was 77.604% (Rijal,

2008).

The main hypothesis of a digital image processing

system can be used to detect Indonesian currency

(IDR) and read the nominal value of Indonesian

currency (IDR). In general, Indonesian currency

(IDR) detection and reading of the nominal value of

Indonesian currency (IDR) can be realized with

software using two lighting, namely an ordinary

lighting and an ultra violet lighting with methods for

digital signal processing.

ICE-TES 2021 - International Conference on Emerging Issues in Technology, Engineering, and Science

2 METHODS

In this study, two procedures were used, namely, first

using an ordinary lighting, the second using an ultra

violet lighting.

2.1 Indonesian Currency (IDR)

Nominal Value Detection System

The process of using an ordinary lighting aims to read

the nominal Indonesian currency (IDR) using the

Naïve Bayes classifier. The system design of this

ordinary procedure can be seen in Figure 1. First to

be done is inputting scanned normal image, then the

system will do pre-processing. Pre-processing is the

process in order to get optimal image results, so that

it is easy to carry out the next process. After doing

pre-processing, then the next step is feature extraction

with analysis using the Grey Level Co-occurrence

Matrix (GLCM) method. From the GLCM analysis,

the entropy value, contrast value, correlation value,

energy value and homogeneity value will be obtained.

After carrying out the feature extraction stage, the

next process will be classified with the naïve Bayes

classifier, after being classified, the final value will be

in the form of a nominal Indonesian currency (IDR).

Figure 1: Ordinary detection system nominal value for

money.

The second procedure is using an ultraviolet

lighting which aims to detect the validity of the

Indonesian currency (IDR). First of all to be done is

that the money is irradiated with ultra violet then

photographed and get a UV image, then the ultra

violet image will be passed in the pre-processing

process, namely crop at the water point you want to

detect, after the pre-processing stage, then the

features will extracted for each Red, Green, image.

and Blue and will produce the mean r, mean g and

mean b feature values. After getting the mean R, G

and B feature values, it will be classified with the

naïve Bayes classifier. Then the output will be

generated whether the money is real or fake.

2.2 Nominal Value in Indonesian

Currency (IDR)

Indonesian banknotes are money in the form of sheets

made of paper issued by the Indonesian government,

in this case Bank Indonesia, whose the usage is

protected by Law No.23 of 1999 and legally used as

a means of exchange for payments within the territory

of the Republic of Indonesia. In this study, the money

that will be examined is the Indonesian banknotes

from 2004 to 2016 in the form of Rp. 1,000, Rp.

2,000, Rp. 5,000, Rp. 10,000, Rp. 20,000, and Rp.

50,000, Rp. 75,000, and Rp. 100,000 (Figure 2).

There are several characteristics of the

authenticity of Indonesian currency (IDR), prior to

2016, namely: 1. Recto verso: the BI logo which will

be completely visible when exposed; 2. Latent Image:

the hidden BI logo can be seen from a certain point of

view; 3. Watermark: in the form of a picture of a

National Hero that will be visible when viewed; 4.

Security thread: thread embedded in the paper bearing

the inscription of Bank Indonesia and flashing red

when under UV light; 5. Micro-writing: Bank

Indonesia writing which can only be read with the

assistance of Loupe; 6. Micro letters: BI letters which

can only be read with the help of Loupe; 7. Serial

number: consists of 3 letters and 6 numbers which

will change colour when under UV light; 8. Blind

code that has the shape of a rectangular box, two

squares, a circle, two triangles and two circles that

feels rough when touched; 9. Visible ink: ink in the

form of Kalimantan ornaments, Palembang

ornaments, Balinese ornaments that will brighten

colours when under UV light; 10. Invisible ink:

nominal numerical ink that will glow when under UV

light; 11. Print a rainbow in a rectangular shape that

will change colour when viewed from a different

perspective.

Take Image

with Camera

Preprocessing

GLCM

Naïve Bayes

Rupiah

Nominal Value

Exploration of an Indonesian Currency Legality Detection System by Utilizing Image Intensity of RGB Mean Values

Figure 2: Indonesian banknotes (a) after 2016 and (b)

before 2016.

2.3 Indonesian Currency (IDR)

Legality Detection System

The process of legality of the Indonesian currency

(IDR) can be seen when using an ultra violet lighting.

Ultra violet light hitting the special ink on the

Indonesian currency (IDR) will cause the appearance

of the banknotes to differ between fake and real

money.

Figure 3 is an example of the appearance of real

banknote when viewed with an ultra violet lighting,

and Figure 4 is an example of the appearance of fake

banknote when viewed with an ultra violet lighting.

Figure 5 is banknote photographed with a regular

lighting. The full design of the proposed Indonesian

Currency (IDR) legality detection system can be seen

in Figure 6. The presence of special inks used causes

authenticity can be distinguished by calculating the

mean value of each component Red, Green, and Blue.

Figure 3: Valid Indonesian currency (IDR) as seen in ultra

violet light.

Figure 4: Fake Indonesian currency (IDR) seen in ultra

violet light.

Figure 5: Valid Indonesian currency (IDR) seen with a

ordinary lighting.

2.4 Gray-Level Co-occurrence Matrix

(GLCM)

One approach in describing texture is to use a

statistical moment of histogram of the intensity of an

image. Statistics method such as a matrix of shared

events are important to get valuable information

about the relative position of neighbouring pixels of

an image (Eleyan & Demirel, 2011) this method is

used to identify textiles by (Azim, 2015). Co-

occurrence matrix P is defined as described in

equation 1.

The reviews of some features of a digital image

by using GLCM are also given in this sub-section.

Those are Energy, Contrast, Correlation, and

Homogeneity (features vector). The energy known as

uniformity of ASM (Angular Second Moment)

calculated as given in equation 2.

ICE-TES 2021 - International Conference on Emerging Issues in Technology, Engineering, and Science

Energy =

∑





∑





𝑃𝑖,

𝑗





(2)

Figure 6: Indonesian Currency (IDR) legality detection

system.

Contrast measurements of texture or gross

variance, of the grey level. The difference is expected

to be high in a coarse texture if the grayscale contrast

is significant local variation of the grey level.

Mathematically, this feature is calculated as defined

in equation 3.

Contrast =







∑





∑





𝑖 

𝑗





𝑝𝑖,

𝑗



(3)

Texture correlation measures the linear

dependence of grey levels on those of neighbouring

pixels. This feature is computed as defined in

equation 4.

Correlation =

∑





∑









,



















(4)

Where:

𝜇









𝑀





𝑝



𝑚,𝑛



𝜇









𝑁





𝑝



𝑚,𝑛



𝜎









𝑚  𝜇













𝑝𝑚,𝑛

𝜎











𝑛  𝜇















𝑝𝑚,𝑛

(5)

(6)

(7)

(8)

The homogeneity measures the local correlation a

pair of pixels. The homogeneity should be high if the

grey level of each pixel pair is similar. This is

calculated by the function in equation 9.

Homogeneity =

∑





∑





,











(9)

2.5 Naïve Bayes Classifier

The Naive Bayes algorithm is a simple probabilistic

classifier that calculates a set of probabilities by

counting the frequency and combinations of values in

a given data set. The algorithm uses Bayes theorem

and assumes all attributes to be independent given the

value of the class variable. This conditional

independence assumption rarely holds true in real

world applications, hence the characterization as

Naive yet the algorithm tends to perform well and

learn rapidly in various supervised classification

problems. Naïve Bayesian classifier is based on

Bayes’ theorem and the theorem of total probability.

The probability that a document d with vector 𝑥

〈𝑥



,…𝑥



〉 belongs to hypothesis h is (Patil &

Sherekar, 2019)

The Bayes Theorem formula is defined in

equation 10 (Patil & Sherekar, 2019).

𝑃



𝑄|𝑋





𝑃



𝑋|𝑄



.𝑃



𝑄



𝑃



𝑋



(10)

Where: 𝑋 Data with unknown class; 𝑄 The

hypothesis 𝑋 is a specific class; 𝑃



𝑄|𝑋



The

probability of the Q hypothesis refers to 𝑋; 𝑃



𝑄



Probability of the hypothesis 𝑄 (prior probability);

𝑃



𝑋|𝑄



Probability 𝑋 in the hypothesis 𝑄 ; 𝑃



𝑋



Probability 𝑋.

To explain the Naïve Bayes theorem, it must be

known that the classification process requires various

clues to determine the class according to the sample

analysed. Therefore, the Bayes theorem above is

adjusted as given in equation 11.

𝑃



𝑖,

𝑗















 1 𝑖𝑓 𝐼



𝑥,𝑦



𝑖 𝑎𝑛𝑑

𝐼𝑥  ∆_𝑥,𝑦  ∆_𝑦  

𝑗

 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(1)

Take image

money with ultra

violet lighting

Pre-processing

Mean Red, Mean

Green, Mean Blue

Naïve Bayes

Rupiah

Validity

Exploration of an Indonesian Currency Legality Detection System by Utilizing Image Intensity of RGB Mean Values

𝑃



𝑄|𝑋



…𝑋







𝑃



𝑋



…𝑋



|𝑄



.𝑃



𝑄



𝑃



𝑋



…𝑋





(11)

Where: 𝑄 variable is a representation of class,

while variable 𝑋



…𝑋



represents the characteristics

of the instructions needed for the classification

process.

2.6 Mean Value

The mean value of each image intensity on the Red,

Green and Blue channels is calculated by using the

equation in 12 (Ni’am, 2013):

𝜇



𝑓



𝑝



𝑓





(12)

Where: 𝑓



is a grey intensity value, while 𝑝



𝑓





is the

histogram value (probability of the intensity

appearing in the image).

2.7 Success Rate

The success rate is measured using the f-measure in

equation 15.

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 

𝑇𝑃

𝑇𝑃  𝐹𝑃

𝑟𝑒𝑐𝑎𝑙𝑙 

𝑇𝑃

𝑇𝑃  𝐹𝑁

𝑓𝑚𝑒𝑎𝑠𝑢𝑟𝑒2

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 .𝑟𝑒𝑐𝑎𝑙𝑙

𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛  𝑟𝑒𝑐𝑎𝑙𝑙

(13)

(14)

(15)

Where:

TP (True Positive) is a test to read the nominal

value of banknotes based on the system and manual

is correct. FP (False Positive) is a test to read the

nominal value of banknotes based on the system that

is not correct, but based on manually it is correct. FN

(False Negative) is a test to read the nominal value of

banknotes based on the system and manual is wrong.

3 RESULTS AND DISCUSSION

3.1 Classification of Indonesian

Currency (IDR) Nominal Value

In the process of detecting the nominal value of the

Indonesian currency (IDR), it has been successfully

realized. At first, image of the money taken by the

camera or scanned by a scanner, then the results of

the photo or scan was entered in the program as

shown in Figure 7.

Figure 7: Photograph or scan image.

The image will be cropped on the left side, namely

on the part where there is a nominal, after that the

RGB image was changed to a grayscale image and the

intensity value is being adjusted. The results can be

seen in Figure 8.

Figure 8: Image of cropped money.

It was continued with segmentation of grayscale

images into foreground and background parts using

active contour as shown in Figure 9.

Figure 9: Image of the active contour.

Followed by filling in the blanks in the image

with the morphological structuring element

technique. Images other than the nominal value will

be removed using a dilation and erosion operation.

Neighbouring pixels located 2 pixels from the

element's centre point will be assigned a value equal

to the binary value of the element's centre point.

Figure 10: Binary image.

ICE-TES 2021 - International Conference on Emerging Issues in Technology, Engineering, and Science

Figure 12: Output image of classification program.

The results of this binary image are used to

calculate entropy, contrast, correlation, energy, and

homogeneity. This value can be seen in Figure 11.

Figure 11: Entropy, contrast, correlation, energy, and

homogeneity value, respectively.

In the training process, 6 images are used for each

Indonesian currency (IDR) with the same nominal

value so that from 15 Indonesian banknotes there are

6 x 15 = 90 GLCM training data with supervised

learning. The data is stored in a nominal.txt and

class.txt file. In the testing process, the file is used as

reference data for classifying nominal values. The

classification here uses Naïve Bayes and the system

will display the results of the test image data

classification by displaying the nominal value of the

prediction results of Naïve Bayes as a nominal value

of 100 000 as in Figure 12.

In the test results with 30 test data, it is obtained

that TP = 24, FP = 6, FN = 0, so that from the f-

measure value, the percentage of system accuracy for

reading the nominal value of Indonesian banknotes is

88%.

3.2 Classification of Rupiah Validity

The process of detecting the validity of the rupiah

currency has been successfully realized. At first the

money was exposed to ultra violet light and

photographed, and then the photos or scans were

included in the program as shown in Figure 13.

Figure 13: Ultra violet image.

The ultra violet image result cropped at position

[0.5 0.5 1894 1730] in pixels and the results can be

seen in Figure 14.

Figure 14: Cropped ultra violet image.

The cropped UV image was separated by Red,

Green, and Blue channels, and then the mean

intensity of each channel is calculated. The data

stored in a file data_uv.txt and class_uv.txt. In the

testing process, the file used as reference data to

classify genuine or fake. The classification here uses

Naïve Bayes and the system will display the results of

Exploration of an Indonesian Currency Legality Detection System by Utilizing Image Intensity of RGB Mean Values

the classification of the test image data by displaying

the original or false information from the prediction

of Naïve Bayes as in Figure 15.

Figure 15: Program output for the validity of the Indonesian

currency (IDR).

The results of the ultra violet image for fake

Indonesian currency (IDR), which is different from

the ultra violet image of the original Indonesian

currency (IDR), can be seen in Figure 16 that the fake

banknote is dominated by blue when viewed with

ultra violet light.

Figure 16: UV image of ‘fake’ banknote

The cropped UV image at position [0.5 0.5 1894

1730] in pixels can be seen in Figure 17. The cropped

UV image is separated by Red, Green, and Blue

channels, and then the mean intensity of each

channel is calculated. The data is stored and

combined with the cropped UV image in a

data_uv.txt and class_uv.txt file. In the testing

process, the file is used as reference data to classify

genuine or fake. The classification here uses Naïve

Bayes and the system will display the results of the

classification of the test image data by displaying the

original or false information from the prediction of

Naïve Bayes as in Figure 18.

In the training process, 6 images are used for each

Indonesian currency (IDR) with the same nominal

value so that from 15 Indonesian banknotes there are

6 x 15 = 90 training data mean Red, mean Green, and

mean Blue with supervised learning. In the test results

with 30 test data, we get TP = 30, FP = 0, FN = 0, so

we get precision = 1, recall = 1, f-measure = 1. From

the f-measure value, the percentage of system

accuracy for reading the nominal Indonesian

banknotes is 100%.

Figure 17: Ultra violet crop image of a ‘fake’ banknote.

Figure 18: Mean red, mean green, mean blue prediction of

a ‘fake’ banknote.

4 CONCLUSIONS

The nominal value of Indonesian banknotes are Rp.

1000, Rp. 2000, Rp. 5000, Rp. 10000, Rp. 20000, Rp.

50000, Rp. 75000, and Rp. 100000 in the 2004-2021

edition has been successfully realized using the Grey-

Level Co-Occurrence Matrix (GLCM). Several

features of digital images, namely energy, contrast,

correlation, and homogeneity, are used as input for the

Naïve Bayes Classifier. From the experimental results

obtained 88% accuracy and the validity of the

Indonesian currency (IDR) can be detected using the

mean of red, mean of green, and mean of blue channel

values.

ACKNOWLEDGEMENTS

Thank you so much for the funding support that

makes this research possible to Universitas Kristen

Maranatha.

ICE-TES 2021 - International Conference on Emerging Issues in Technology, Engineering, and Science

REFERENCES

Alshayeji, M. H., Al-Rousan, M., & Hassoun, D. T. (2015).

Detection method for counterfeit currency based on bit-

plane slicing technique. International Journal of

Multimedia and Ubiquitous Engineering, 10(11), 225–

242. https://doi.org/10.14257/ijmue.2015.10.11.22

Azim, G. A. (2015). Identification of Textile Defects Based

on GLCM and Neural Networks. Journal of Computer

and Communications, 03(12), 1–8.

https://doi.org/10.4236/jcc.2015.312001

Eleyan, A., & Demirel, H. (2011). Co-occurrence matrix

and its statistical features as a new approach for face

recognition. Turkish Journal of Electrical Engineering

and Computer Sciences, 19(1), 97–107.

https://doi.org/10.3906/elk-0906-27

Indradewi, I. G. A. A. D., & Ariantini, M. S. (2019).

Jaringan Syaraf Tiruan LVQ Berbasis Parameter HSV

dalam Penentuan Uang Rupiah Palsu. Jurnal Ilmiah

Teknologi Informasi Asia, 13(1), 47-52.

Ni’am, B. A. A. A. (2013). Identifikasi nilai nominal dan

keaslian uang kertas rupiah menggunakan support

vector machine (Bachelor thesis, Universitas Islam

Negeri Maulana Malik Ibrahim).

Pambudi, A. R., Informatika, T., Komputer, F. I.,

Karawang, U. S., Segmentasi, M., Based, R., Contour,

A., Tepi, D., Metode, M., & Dengan, C. (2016). Deteksi

keaslian uang kertas berdasarkan watermark dengan

pengolahan citra digital. Jurnal Informatika

Polinema, 6(4), 69-74.

Patil, T. R., & Sherekar, S. S. (2019). Performance Analysis

of ANN and Naive Bayes Classification Algorithm for

Data Classification. International Journal of Intelligent

Systems and Applications in Engineering, 7(2), 88–91.

https://doi.org/10.18201/ijisae.2019252786

Prasanthi, B. S., & Setty, D. R. (2015). Indian Paper

Currency Authentication System-A Quick

Authentication System. 6(9), 1249–1256.

Rijal, Y. (2008). Identifikasi Keaslian Mata Uang Rupiah

Melalui Invisible Ink Berbasis Fourier Transform

Menggunakan Neural Learning Vector Quantization.

Retrieved from Repositori Universitas Dinamika:

https://repository.dinamika.ac.id/id/eprint/ 425/1/2008-

IV-396.pdf.

Giri, I. K. Y. B. (2019). Pendeteksian Mata Uang Rupiah

Palsu Menggunakan Image Processing. Retrieved from

Sekolah Teknik Elektro dan Informatika (STEI) ITB:

https://informatika.stei.itb.ac.id/~rinaldi.munir/Citra/2

019-2020/Makalah2019/13516115.pdf.

Exploration of an Indonesian Currency Legality Detection System by Utilizing Image Intensity of RGB Mean Values