







































 P-ISSN : 2715-2448 | E-ISSN : 2715-7199  

Vol.4 No.1 January 2023 

Buana Information Technology and Computer Sciences (BIT and CS) 

 

1 | Vol. 4 No.1, January 2023 

 

Coastal Batik Motifs Identification Using K-Nearest Neighbor Based  

On The Grey Level Co-Occurrence Method 
 

 
Wresti Andriani1 

Informatics Engineering 

STMIK YMI Tegal 

Email: wresty.andriani@gmail.com 
 

Gunawan2* 

Informatics Engineering 

STMIK YMI Tegal 

Email: gunawan.gayo@gmail.com 
 

Sawaviyya Anandianskha3 

Informatics Engineering 

STMIK YMI Tegal 

Email: sayaviyyaa@gmail.com 

 

‹β› 
 

 

Abstrak-Indonesia merupakan negara yang kaya akan 

sumber daya alam, budaya dan pariwisata. Salah satu 

warisan budaya manusia yang terkenal di Indonesia adalah 

batik. Batik memiliki keunikan motif yang sangat beragam 

sehingga sulit untuk mengenali golongan tertentu terutama 

generasi muda. Penelitian ini dilakukan untuk 

mengklasifikasikan batik pesisir, khususnya batik Tegal, 

batik Pekalongan, dan batik Cirebon sehingga dapat 

membantu memudahkan pengenalan dan pemahaman 

batik pesisir jika dibandingkan dengan batik pedalaman, 

seperti batik Yogyakarta. Metode yang digunakan adalah 

Gray Level Co-occurrence Matrix (GLCM) untuk 

mengekstrak fitur tekstur, sedangkan untuk menentukan 

kedekatan citra uji dengan data latih menggunakan metode 

K-Nearest Neighbor (KNN), perhitungan jarak yang 

digunakan adalah Euclidean Distance dan Manhattan 

Distance berdasarkan karakteristik tekstur dari citra batik 

yang diperoleh. Hasil yang diperoleh pada penelitian ini 

dimana skor tertinggi adalah 64% untuk Euclidean 

Distance dan 66% untuk Manhattan Distance pada k = 15. 

Kata Kunci— Pesisir, GLCM, KNN, Identifikasi 

Abstract-Indonesia is a country rich in natural resources, 

culture, and tourism. One of the famous human cultural 

heritage in Indonesia is batik. Batik has unique motifs that are 

very diverse, making it difficult to recognize certain groups, 

especially the younger generation. This research was conducted 

to classify coastal batik, especially Tegal batik, Pekalongan 

batik, and Cirebon batik so that it can help facilitate the 

introduction and understanding of coastal batik when compared 

to inland batiks, such as Yogyakarta batik. The method used is 

the Grey Level Co-occurrence Matrix (GLCM) to extract 

texture features, while, to determine the proximity of the test 

image to the training data using the K-Nearest Neighbor (KNN) 

method, the calculation of the distance to be used is the 

Euclidean Distance and Manhattan Distance based on the 

texture characteristics of the batik image obtained. The result 

obtained in this study where the highest score of 64% for 

Euclidean Distance and 66% for Manhattan Distance at k=15. 

Keywords— Coastal, GLCM, KNN, Identification  

 

I. INTRODUCTION 

Indonesia is a country rich in culture and beautiful nature. 

One of the wealth that is owned is the batik culture. 

Indonesian batik varies according to the many cultures that 

are owned in each region in Indonesia. Batik is an art that 

produces picture cloth which is the original heritage of the 

Indonesian nation and is one of the world heritages and has 

been inaugurated by UNESCO, the United Nations world 

agency in the fields of culture and education. Based on the 

region, batik can be divided into two types, namely coastal 

batik and inland batik and based on the manufacturing 

process, batik is also divided into two, namely written batik, 

namely batik made by writing use liquid "wax" on cloth and 

stamp batik, namely batik made by depicting cloth using a 

stamp that has a pictorial part and is shaped like a relief and 

inscribed using wax liquid, then processed in a certain way. 

Each batik from various regions has its own uniqueness and 

high traditional artistic value. The diversity of traditional 

batik motifs is due to differences in geography, flora and 

fauna, differences in lifestyle and livelihoods. In the past, 

batik work was often done by women and was an exclusive 

job, because the batik cloth produced was presented to the 

nobility or distinguished guests. 

Coastal batik motives are batik motives produced in coastal 

areas such as the Tegal coast, Pekalongan coast, and Cirebon 

coast, while the motives can vary, usually in addition to being 

influenced by geography and native culture, but also often 

influenced by culture brought by fishermen or traders from 

outside the area, making coastal fabric motives more diverse 

compared to batik motifs from the interior, such as batik 

motives from Yogyakarta which are more influenced by the 

sense of nobility of the palace. 

The wide variety of traditional batik motifs in Indonesia 

confuses traders and the younger generation (millennials) in 

recognizing the origin of existing batik fabrics. Because there 

is no center or office that specifically handles and provides 

information about the history of batik itself. In this paper, the 

researcher hopes to help facilitate the millennial generation 

and related parties to become more familiar with motifs so 

that they can love these traditional products more, especially 

Tegal batik, Pekalongan batik, and Cirebon batik. 

This study, will use the Gray Level Co-Occurrent Matric 

(GLCM) method as its feature extraction and use the K-

mailto:wresty.andriani@gmail.com
mailto:gunawan.gayo@gmail.com
mailto:sayaviyyaa@gmail.com


2 | Vol. 4 No.1, January 2023 

 

Nearest Neighbor (KNN) method. The distance calculation 

uses Euclidean Distance and Manhattan Distance to 

determine the proximity of the test image to the training data 

and is expected to help identify and classify the image of batik 

motives. The well-identified image of batik will provide clear 

information and can be used for the preservation of 

Indonesian batik fabric motifs from extinction. 

Similar studies that have been conducted by some researchers 

using batik objects include Zulfrianto Y. Lamasigi [1] DCT 

For Extraction Of GLCM-Based Features on Batik 

Identification Using K-NN. The highest accuracy obtained by 

DCT-GLCM exists at an angle of 135° with a value of k=3 of 

64.88% and a value of 64.88% and at an angle of 0° with 

values k=7 and 9 are 41.86%. Frisnanda Aditya, etc [2] 

Pekalongan Batik Identification Using the Grey Level Co-

Occurrence Matrix and Probabilistic Neutral Network 

Method. From the results of this classification test, the best 

accuracy is 61.33%. There is has been no research that 

examines the identification of the difference between coastal 

and inland batik using GLCM and KNN method before. 

II. METHOD 

A. Gray Level Co-occurrence Matrix (GLCM)  

Grey Level Co-occurrence Matrix (GLCM)was first 

submitted by Haralick in 1979 with 28 features to explain 

spatial patterns. There are several steps that are taken, 

namely: first, calculating the features of the GLCM by 

converting an RGB image into a grey scale image. Second, 

creating a co-occurrence matrix is continued by determining 

the spatial relationship between reference pixels and 

neighbouring pixels based on angle 𝜃 and distance d. Third, 

creating a symmetrical matrix by adding a co-occurrence 

matrix to the transpose matrix. Fourth, the symmetrical 

matrix is normalized by calculating the probability of each 

element of the matrix. Fifth, calculate the features of the 

GLCM. Each feature is calculated by one pixel distance in 

four directions, i.e. 00, 450, 900, and 1350 to detect co-

occurrence [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12].  

There are 5 GLCM features used in this study, including:  

1) Angular Second Moment (ASM) 

ASM is a measure of the homogeneity of the image. 

𝐴𝑆𝑀 = ∑ ∑ (𝐺𝐿𝐶𝑀 (𝑖, 𝑗))2𝐿
𝑗=1

𝐿
𝑖=1  (1) 

2) Contrast 

Contrast is a measure of the presence of variations in the grey 

level of images. 

𝐶𝑜𝑛𝑡𝑟𝑎𝑠𝑡 = ∑ ∑ |𝑖 − 𝑗|2𝐺𝐿𝐶𝑀 (𝑖𝑗)𝐿
𝑗

𝐿
𝑖  (2) 

3) Inverse Different Moment (IDM) 

Used to measure homogeneity 

𝐼𝐷𝑀 = ∑ ∑
(𝐺𝐿𝐶𝑀(𝑖,𝑗))2

1+(𝑖−𝑗)2
𝐿
𝑗=1

𝐿
𝑖=1  (3) 

4) Entropy 

Entropy for represents the measure of the grey level 

irregularity in the image. 

𝐸𝑛𝑡𝑟𝑜𝑝𝑖 = − ∑ ∑ (𝐺𝐿𝐶𝑀 (𝑖, 𝑗)) log(𝐺𝐿𝐶𝑀 (𝐼, 𝐽))𝐿
𝑗=1

𝐿
𝑖=1  (4) 

5) Correlation 

Correlation is a measure of the dependence between the grey 

values in the image. 

𝐶𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 = ∑ ∑
(𝑖−𝜇𝑖′)(𝑗−𝜇𝑗′)(𝐺𝐿𝐶𝑀 (𝑖,𝑗))

𝜎𝑖𝜎𝑗

𝐿
𝑗=1

𝐿
𝑖=1   (5) 

This equation is based on the mean value of the grey image 

intensity and the standard deviation. The standard deviation 

is obtained from the square root of the variant which shows 

the distribution of pixel values in the image, with the 

following formula: 

𝑚𝑒𝑎𝑛 𝑖 =  𝜇𝑖′ = ∑ ∑ 𝑖 ∗ 𝐺𝐿𝐶𝑀(𝑖, 𝑗)
𝐿

𝑗=1

𝐿

𝑖=1
 

 𝑚𝑒𝑎𝑛 =  𝜇𝑗′ =  ∑ ∑ 𝑗 ∗ 𝐺𝐿𝐶𝑀(𝑖, 𝑗)
𝐿

𝑗=1

𝐿

𝑖=1
 

𝑣𝑎𝑟𝑖𝑎𝑛 𝑖 =  𝜎𝑖2 =  ∑ ∑ 𝐺𝐿𝐶𝑀(𝑖, 𝑗)(𝑖 − 𝜇𝑖′)2
𝐿

𝑗=1

𝐿

𝑖=1
 

𝑣𝑎𝑟𝑖𝑎𝑛 𝑗 =  𝜎𝑗2 =  ∑ ∑ 𝐺𝐿𝐶𝑀(𝑖, 𝑗)(𝑗 − 𝜇𝑗′)2
𝐿

𝑗=1

𝐿

𝑖=1
 

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑖 =  𝜎𝑖 =  √𝜎𝑖2  

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑗 =  𝜎𝑗 =  √𝜎𝑗2  

B. K-Nearest Neighbor 

K-Nearest Neighbour (KNN) is a method that uses a 

supervised algorithm the results of the newly classified query 

instance based on the majority of the categories on the KNN. 

This algorithm aims to classify new objects based on 

attributes and training samples. The KNN algorithm is very 

simple, based on the shortest distance from the query instance 

to the training sample to determine its KNN. The training 

sample is projected into a multi-dimensional space, where 

each dimension represents a feature of the data. Space is 

divided into sections-part based on the classification of the 

training sample. A point in this space is marked class c if class 

c is the most common classification in the k nearest neighbour 

of that point. 

1) Euclidean Distance 

Calculation of the distance of Euclidean Distance which is 

represented as follows [3] [13]: 

𝑑 =  √(𝑎1 − 𝑏1)2 + (𝑎2 − 𝑏2)2 + ⋯ + (𝑎𝑛 − 𝑏𝑛)2 

𝑑 =  √∑ (𝑎𝑖 − 𝑏𝑖)2𝑛
𝑖=1  (6) 

Where d (a, b): The Euclidean distance between the vector a 

and the vector b, ai: feature vector a, bi: features of the vector 

b, n: the number of features in the a and b vectors. 

2)   Manhattan Distance 

Manhattan or City Distance is a similarity measurement that 

is most suitable for project approvals that represent relevant 

cases with natural numbers or with quantitative data. Also 

used to retrieve matched cases from the case base by 

calculating the absolute weighted sum of the differences 

between the current case and other cases the case base. To 

calculate the weight, the following quationis used:  



3 | Vol. 4 No.1, January 2023 

 

𝑑𝑖𝑗 = ∑ 𝑊𝑘 |𝑥𝑖𝑘 − 𝐶𝑗𝑘|  (7) 

𝑑𝑖𝑗 = 𝑑𝑒𝑠𝑡𝑎𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑐𝑎𝑠𝑒𝑠 𝑖 𝑎𝑛𝑑 𝑗  

𝑊 = 𝑟𝑒𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑡ℎ𝑒 𝑠𝑢𝑚 𝑜𝑓 𝑤𝑒𝑖𝑔ℎ𝑡 

𝑋 = 𝑛𝑒𝑤𝑙𝑦 𝑟𝑒𝑑𝑢𝑐𝑒𝑑 𝑐𝑎𝑠𝑒 𝑤𝑖𝑡ℎ 𝐶 

C. Confusion Matrix 

The confusion matrix is a table consisting of many rows of 

data test that predicted true and false by the classification 

model, to determine the performance of a classification 

model [14] [15]. 

Table I. Confusion Matrix 

 Predicted Class 

Actual Class Class Class = 1 Class = 0 

Class = 1 F 11 F 10 

Class = 0 F 01 F 00 

Accuracy calculation using confusion matrix as follows: 

𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =  
𝐹11+𝐹00

𝐹 11+𝐹10+𝐹01+𝐹00
  

or 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =  
𝑁𝑢𝑚𝑏𝑒𝑟 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑡𝑟𝑢𝑒

𝑛𝑢𝑚𝑏𝑒𝑟 𝑑𝑎𝑡𝑎
 𝑥 100% 

The flow/stages that will be done in this study can be seen in 

Figure 1 below:  

 
Figure 1. Research Flowchart 

III. RESULTS AND DISCUSSION 

Data collection (data collection) is obtained from literature 

study, observation, and data search in the form of batik 

motive images via the internet. Image data obtained are as 

many as 90 batik motives, consisting of coastal batik from the 

city of Tegal as many as 20 batik motifs, 50 kinds of 

Pekalongan batik motifs, and 20 kinds of Cirebon batik 

motives, also 10 kind of Yogyakarta batik motives. Batik 

Yogyakarta as representative of inland batik. All the batik 

data motives obtained are converted into the same size and 

extension * jpg. This data image is then grouped into training 

data. Yogyakarta batik motif images. All of the test data is the 

same size and also has a jpg extension. 

Below are some examples of images of batik motives that 

have been collected: 

   
(a) 

   
(b) 

   
(c) 

   
(d) 

Figure 2. Example Batik (a) From Tegal (b) From 

Pekalongan (c) From Cirebon (d) From Yogyakarta 

Each image is done with the feature extraction process using 

GLCM using MATLAB 2020. From MATLAB results, then 

feature extraction is classified using KNN algorithm with 2 

kinds of distance measurement, namely Euclidean Distance 

and Manhattan Distance and using rapid miner tool.  

A. Texture Feature Extraction  

In the previous process, we obtained a total of 90 data of batik 

motifs as training data consisting of 20 Tegal batik motifs, 50 

kinds of Pekalongan batik motifs and 20 kinds of Cirebon 

batik motifs along with 10 kinds of batik motifs from 

Yogyakarta as samples of inland batik motifs as a 

preprocessing stage. The results of this stage are then 

continued at the extraction stage of the texture feature. The 

extraction of this feature is done using MATLAB 2020 which 

results in Table II below: 

 
Table II. GLCM Results 

No Matrix 
Eccentat

ion ... 
Next ... Korel ... Energy Homo… 

1 116,570 68,016 0.617 0.005 5,453 0.066 

2 90,403 42,895 0.148 0.006 5,134 0.028 

3 132,540 56,192 0.210 0.005 5,338 0.046 

4 104,627 72,138 0814 0.005 5,318 0.074 

5 187,068 46,154 1,568 0.008 5,062 0.032 

⋮       

90 93,465 46,579 0.464 0.006 5,185 0.032 

Once the GLCM feature extraction results using MATLAB 

2020 are obtained, the feature extraction data is obtained from 

GLCM, then the data is entered into the rapid miner tool and 



4 | Vol. 4 No.1, January 2023 

 

using the Loop Parameter, it can be determined the value k = 

1,3,5,7,9,11,13,15 for used, the k which produces the highest 

accuracy value to be used from the distance method, The 

method used are Euclidean Distance and Manhattan (City) 

Distance. The results can be seen in the Table 3 below:  

Table III. Comparison between the Manhattan and Euclidean 

Methods 

Accuracy 

Distance k=1 k=3 k=5 k=7 k=9 k=11 k=13 k=15 

Manhattan 55 60 61 62 59 61 57 66 

Euclidean 52 59 60 61 63 62 62 64 

The comparison of accuracy in Table III between Euclidean 

Distance and Manhattan Distance above, a graphic image is 

obtained as shown in Figure 3. 

 
Figure 3. Comparison of Manhattan and Euclidean 

In the Table III and Figure 3, it can be seen that the 

comparison from Euclidean Distance and Manhattan 

Distance, both of them have the highest accuracy at the value 

k = 15, where the highest accuracy is obtained at 64% for 

Euclidean Distance and is 66% for Manhattan Distance. 

The same occurrence for the smallest accuracy value is 

obtained at the value k=1. Manhattan's accuracy is 55% and 

Euclidean’s is 52%.  

B. Confusion Matrix 

In this study, in addition to knowing the comparison between 

Euclidean Distance and Manhattan City Distance, it will also 

be seen how accurate the coastal batik is with the inland batik 

by knowing the confusion matrix. The result of the Confucius 

matrix from the classification of coastal batik with inland 

batik, in this case, represented by Tegal batik, Pekalongan 

batik, and Cirebon batik for coastal batik and Yogyakarta 

batik as inland batik can be seen from the following Table IV. 

  
Table IV. Prediction Motive Batik Performance 

 
True 

Cirebon 

True 

Yogyakarta 

True 

Pekalongan 

True 

Tegal 

Class 

Precission 

Pred. 

Cirebon 
8 1 3 1 61,54% 

Pred. 

Yogyakarta 
0 1 0 4 20,00% 

Pred 

Pekalongan 
8 2 44 4 75,86% 

Pred. Tegal 4 6 3 11 45,83% 

Class Recall 40% 10% 88% 55% 45,83% 

When the prediction results above are tested using the 

confusion matrix obtained results in Table V below: 

Table V. Matrix of the Predictions 

No. Label Prediction Result 

1 Cirebon Pekalongan 0 

2 Cirebon Pekalongan 0 

3 Yogyakarta Pekalongan 0 

4 Pekalongan Pekalongan 1 

5 Pekalongan Pekalongan 1 

6 Pekalongan Pekalongan 1 

7 Pekalongan Pekalongan 1 

8 Pekalongan Pekalongan 1 

9 Tegal Pekalongan 0 

10 Tegal Pekalongan 0 

11 Cirebon Pekalongan 0 

12 Cirebon Pekalongan 0 

13 Yogyakarta Pekalongan 0 

14 Pekalongan Pekalongan 1 

15 Pekalongan Pekalongan 1 

16 Pekalongan Pekalongan 1 

17 Pekalongan Pekalongan 1 

18 Pekalongan Pekalongan 1 

The number 0 indicates the wrong label prediction, the 

number 1 indicates the correct prediction. The accuracy of 

predictions using Manhattan (City) Distance is higher than 

Euclidean Distance which is 66%. From the batik motif data 

that has been collected, there is a prediction that the true batik 

from Pekalongan is higher 75.86%, than the true batik from 

Cirebon is 61.54%. It can be concluded data from the training 

data obtained, after being tested was more predicted as 

Pekalongan batik, then followed by Cirebon batik. 

IV. CONCLUSION 

Based on the results of the above research, which uses 90 

training data consisting of 20 data Tegal batik motifs, 50 data 

Pekalongan batik motifs, and 20 Cirebon batik motifs, as well 

as 40 test data, consisting of 10 data of original Tegal batik 

motifs, 10 Pekalongan batik motifs, can be concluded: 

1) The accuracy of Cirebon batik motifs and Yogyakarta 

batik motifs using Manhattan distance method is better 

than Euclidean Distance, which is 66%. 

2) The value k=1 of Manhattan Distance and Euclidean 

Distance is the smallest is 54% for Manhattan and 52% 

for Euclidean Distance. 

3) Predicted result of batik motif from Pekalongan, which 

is 75% and followed by Cirebon batik by 61.54%, then 

the prediction of batik Tegal is 45.85% and then batik 

Yogyakarta that is equal to 20%. 

For more research, researchers suggested that in testing it is 

recommended to use different object retrieval sizes and use 

different methods from the research created by current 

researchers in order to produce even better accuracy. 

 

REFERENCES 

[1]  F. A. etc, "Pekalongan Batik Identification Using the 

Gray Level Co-Occurrence Matrix and Probabilistic 

Neutral Network Method," e-Proceeding of 

Engineering, vol. 6, p. 10234, 2019.  



5 | Vol. 4 No.1, January 2023 

 

[2]  H. C. D. a. S. A. A. Halim, "Image Retrieval Aplication 

Using a Combination of Color Moment and Gabor 

Texture Methods," JSM STMIK Mikrisil, vol. 14, 2013.  

[3]  A. K. a. A. Susanto, Image Processing Theory and 

Application, Yogyakarta: Andi, 2012.  

[4]  I. S. a. Y. C. AJ Arriawati, "Classification of Texture 

Images Using K-Nearest Neighbor Based on the 

Characteristics Extraction of the Cookbook Matrix 

Method," Diponegoro University, Semarang. 

[5]  N. S. a. A. W. B. Arisandi, "Introduction to Batik Motif 

Using Rotated Wavelet Filters and Neural Networks," 

JUTI, vol. 9, pp. 13-19, 2011.  

[6]  S. D. Cahyo, "Comparative Analysis of Several Edge 

Detection Methods Using Delphi 7," Gunadarma 

University, Depok, 2009. 

[7]  C. C. a. T. B. o. C. Regency, "Casta and Taruna, Batik 

Cirebon, World Cultural Heritage from Indonesia, 

Cirebon," Cirebon, 2007. 

[8]  D. P. Pamungkas, "Image Extraction Using GLCM and 

KNN Methods to Identify Types of Orchids 

(Orchidaceae)," vol. 1, p. 2, 2019.  

[9]  E. Prasetyo, Data Mining Processes Data Into 

Information Using Matlab, Yogyakarta: Andi, 2014.  

[10]  Eliyani, The introduction of ripe papaya fruit levels 

using RGB color-based image processing with k-means 

clustering, Lhokseumawe: Lhokseumawe State 

Polytechnic, 2013.  

[11]  H. Priyanto, Digital Image Processing Theory and Real 

Applications, Bandung: Informatics Bandung, 2017.  

[12]  H. Wijayanto, "Klasifikasi Batik Menggunakan 

Metode K-Nearest Neighbour Berdasarkan Gray Level 

Co-Occurance Matrixes (GLCM)," 2015.  

[13]  J. Ong, "Implementation of the K-Means Clustering 

Algorithm to Determine President University's 

Marketing Strategy," Scientific Journal of Industrial 

Engineering, vol. 12, pp. 10-30, 2013.  

[14]  I. M. Johan Wahyudi, "Introduction to Traditional 

Fabric Image Patterns Using GLCM and KNN," 

JTIULM, vol. 4, pp. 43-48, 2019.  

[15]  M. S. etc, Comparison of Texture and Color Feature 

Extraction for Classification of Lamongan Batik, 

Tuban, 2017.  

[16]  A. H. a. A. P. H. Rangkuti, "Content-Based Drawing of 

Batik," Journal or Computer Science, vol. 10, pp. 925-

934, 2014.  

[17]  A. P. D. &. W. R. Triprasetyo, "Application of 

Trenggalek Batik Pattern Recognition Using Sobel 

Edge Detection and KMeans Algorithm," Generation 

Journal, vol. 2, pp. 25-32, 2018.  

[18]  Z. Y. Lamasigi, "DCT for Feature Extraction based on 

Glcm on Batik Identification Using K-NN," Jambura 

Journal of Electrical and Electronics Engineering, vol. 

3, 2021.  

[19]  N. L. W. S. R. Ginantra, "Detection Of Batik Parang 

Using The Co-Occurrence Matrix And Features 

Geometric Invariant Moment With KNN 

Classification," Lontar Computer, vol. 7, p. 05, 2016.  

 


