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Detection of Malacca Woven Fabric Motifs Using the  

YOLOv4 Method 
 

Adi Semri Neno1, Aviv Yuniar Rahman2, Fitri Marisa3 
1,2,3Department of informatic Engineering, Universitas Widyagama Malang, Indonesia                                                                      

2school Graduate, Doctor of Philosofhy in information & Communication Technology, Asia Universiti, 

Selangor, Malaysia 
1nenoady@gmail.com, 2aviv@widyagama.ac.id, 3fitri@widyagama.ac.id 

 

 

Abstract  

Malacca is one of the districts that has a weaving culture and also produces woven cloth in East Nusa 

Tenggara. The large number of types of woven cloth from each Malacca tribe means that outsiders and 

even native Malacca people are not yet familiar with typical Malacca motifs, therefore a system is 

needed that can help make it easier for people to recognize the types of woven fabric motifs. Malacca 

woven fabric in this study was used to detect the types of woven fabric motifs in Malacca district using 

the YOLOv4 method. The results of detecting Malacca woven fabric motifs correspond to each type of 

woven fabric. Apart from that, the Malacca woven fabric motif detection system with YOLOv4 

technology is an effective and efficient solution in recognizing Malacca woven fabric motifs. Malacca 

woven fabric is classified into four classes with an impressive mAP score of 100%. 

 

Keywords: Object Detection, Identifying, Malacca woven fabric motifs, woven fabric, YOLOv4. 

 

 

I. Introduction 

Woven fabrics are one of Indonesia's valuable cultural heritages, exuding the rich traditions and 

folk arts of each region[1]. One area known for its beautiful and unique woven fabric is Malacca 

Regency, East Nusa Tenggara Province[2]. Malacca woven cloth is also an important symbol in local 

culture, reflecting the history, beliefs and values of its people[3]. 

One of the most famous cultural assets of Malacca Regency is the art of traditional woven cloth. 

Malacca woven cloth is famous for its beautiful and colorful designs[4]. The woven fabric motifs often 

reflect the surrounding culture and nature. In addition, the practice of weaving is a skill that is passed 

down from generation to generation[5]. It helps develop craftsmen's skills and keeps the tradition of 

arts and crafts alive, as it has beautiful and colorful designs, and woven fabric motifs that reflect the 

culture and natural surroundings[6]. Malacca woven cloth not only plays a role as traditional clothing, 

but also has a deeper role[7]. In everyday life. Motifs and patterns resulting from traditional weaving 

techniques become a means of telling ancient stories, local mythology, and passing knowledge between 

generations. 

There are many types of woven cloth motifs from each Malacca tribe, so outsiders and even native 

Malacca people are not yet familiar with the typical Malacca woven cloth motifs[8]. Therefore, it is 

necessary to detect woven fabric motifs which can help make it easier for the public to recognize the 

type of woven fabric motif using the YOLOv4 method[9]. In previous research, Hue, Saturation, Value 

(HSV) and Gray Level Cooccurrence Matrix (GLCM) feature extraction was carried out to identify 

woven fabric motifs in South Central Timor Regency. Research was carried out to identify types of 

woven fabrics in TTS district using the HSV color feature extraction method, and GLCM texture 

characteristics, and to measure the similarity of woven fabrics using the Euclidean distance metho. The 

P-ISSN: 2715-2448 | E-ISSN: 2715-7199 
Vol.5 No.1 Januari 2024 
Buana Information Technology and Computer Sciences (BIT and CS) 

 



Vol.1, No.2 | 46  
 

results obtained in this research obtained a GLCM texture accuracy level for color features of 55%, 

HSV color features of 62.5% and combination of color and texture features of 91.67%[10]. 

The aim of this research is to detect Malacca woven cloth motifs using YOLOv4, so that it can 

help foreigners and native Malacca people to recognize the types of motifs on Malacca woven cloth[11]. 

This research will make a positive contribution to preserving culture, education and economic 

development in Malacca Regency, as well as introducing the beauty of Malacca woven cloth art to the 

wider world through a modern technological approach[12]. 

II. Methods 

In this case, it is a method for detecting Malacca woven fabric motifs using YOLOv4. The 

methodology used in this research is shown in Figure 1. It begins with the first process of literature 

study which will be carried out by researchers to look for references for implementing Malacca woven 

fabric motif detection using YOLOv4. This process is carried out by running a script that has been 

designed by the researcher. Then testing was carried out using a dataset prepared in the form of woven 

fabric. The test results will then be evaluated using the Mean Average Precision parameter. The purpose 

of this evaluation is to compare the results of identifying YOLOv4 objects in Malacca woven fabric 

motifs. 

 

Fig 1.  motifs detection flow Malacca woven fabric using YOLOv4 

1. Literature Review 

In this case the researcher looked for references from several sources related to Yolo. In this 

process, researchers also use references within the limits of only using the Yolo Method. On the other 

hand, researchers also used source journals to look for references in detecting Malacca woven fabric 

motifs. 

2. Data Collection  

The source that has been obtained is the data used in the reference for Yolo object detection of 

various types. The results of the collection will later be implemented into Malacca woven fabric motifs  

YOLOv4. 

3. Pre-Processing 

In this case, pre-processing is a process of classifying woven fabric motifs according to type and 

class. The way to classify woven fabric motifs is to create a bounding box. Where these limits, the box 

will later become a parameter in producing output, namely according to the class of woven fabric motif. 

4. Mhetod Implementation 

The application method used in this process is YOLOv4. The researchers designed code to 

implement YOLOv4 for woven fabric motifs. This will later be executed according to each code that 

has been designed by the researcher. 

5. Yolo Method Training 

The training process in the YOLOv4 method is the process of running the code designed by the 

researcher. The training process involves five categories of Malacca woven fabric motifs Motig_garuda, 



Vol.1, No.2 | 47  

 

Motif_marobo futus, Motif_human and deer, Motif_futus men dataset will be used to test the results of 

this training. 

 

6. Yolo Method Testing 

The implementation method used in this process is using YOLOv4. Researchers designed code to 

implement YOLOv4 for detection. This will later be executed according to each code that has been 

designed by the researcher. 

7. Evaluation 

The final step is the evaluation stage, which involves assessing the results obtained from 

the YOLOv4 small test. In testing, the parameters used for evaluation include Mean Average 

Precision (mAP), which is calculated based on the equation. 

 

mAP = 
1

𝑛
 ∑
𝑖
𝑛

=1 APi [13] 

 

In the equation, “n” represents the actual value, while “i” corresponds to the curve value 

on the precision x and y axes. The resulting plot will involve point interpolation to separate 

the resulting curve from the x and y axes.  

 
III. Result and Discussion 

Table I is the results of the tests carried out. This could explain that starting from 1000 iterations, 

the Garuda motif mAP value is 82.10% of the total between the training data and testing data. Then for 

the Marobo Futus motif, mPA results were obtained at 100% in the detection of Malacca woven fabric 

motif objects. The male futus motif produces an mAP level of 97.65% for object detection in Malacca 

woven fabric motifs. Furthermore, testing on human and deer motives, the final test resulted in an mAP 

score of 65.58%, a fairly large difference between the data used for training. To find out to what extent 

YOLOv4's detection accuracy is accurate, the testing process continues until the 6000th iteration. There 

are differences or discrepancies between the data used for training and testing at the end of the 

evaluation. 

 

Table 1. Resulut From Woven Fabric Motifs 
Type    Iteration     

 1000 2000 3000 4000 5000 6000 

Eagle motif 82.10% 100% 100% 100% 100% 100% 

marobo futus motifs 100% 100% 100% 100% 100% 100% 

Humen and deer motifs 65.58% 100% 100% 100% 100% 100% 

Men’s futus motifs 97.65% 100% 100% 100% 100% 100% 

 

Furthermore, in the 2000 iteration, the Garuda motif already had a mAP value of 100% of the total 

difference or distinction between the data used for training and testing purposes. Then, the Marobo 

Futus motif also has an mAP result of 100% in detecting Malacca woven fabric motifs. The male futus 

motif produces a mAP level of 100% detection of the Malacca woven fabric motif object. Furthermore, 

testing on human and deer motifs had an mAP level of 100% of the total detection. The next test used 

3000 iterations, the results of the Garuda motif had a mAP value of 100% of the total sum of the 

differences between the data used for training and testing purposes. Then the marobo futus motif has a 

result of 100% in the detection of Malacca woven fabric motif objects. The male futu motif produces a 

mAP detection rate of 100%. Woven fabric motif objects. Furthermore, testing on human and deer 

motifs alone had the same mAP level. In detecting other motifs, the mAP level obtained was 100% of 

the total detected. In the 2000th to the 6000th iteration, the mAP level obtained was more stable and 

did not experience a decrease. Various iterations have maximum yields of mAP levels up to 100%



 

Vol.1, No.2 | 48  
 

 

 
Fig 2. mAP Highest Detection of  Malacca woven fabric motifs 

 

 
Fig 3. Detection results of Malacca woven fabric motifs 

 

Table 2. comparison of research methods with the proposed method 

Researcher Object Detection Method mAP 

FS Lesiangi AY 

Mauko And, BS 

Djahi 

1). TTS woven fabric 

2).3 fabric images TTS 

tribal weaving Datasets 

1). Hue, Saturation, Value 

HSV), dan  

55%,-62,5% 

  2). Gray Level Cooccurrence 

Matrix (GLCM) 

91,67%. 

Our Proposal Woven fabric 1500 

datasets 

YOLOv4 100.0% 

 

In Figure 2 it can be explained that the values produced in the Tenu Malaka fabric motif detection 

test with various iterations had maximum results with mAP levels reaching 100.0%. for the time used 

in detection is only a few minutes. However, if detection with more iterations, it will take around 8 

hours to produce Mean Accuracy Precision. Additionally, at the maximum batch used in testing, 14,000 

sampling tests and training data were used. The error rate in the entire test was only 0.522 in the woven 

fabric motif detection test. The tests that have been carried out have the maximum and highest mAP 

values from 1000 iterations to 6000 iterations shown in Figure 3. It can be explained that the mAP at 

1000 iterations is the test results has a maximum value of 97.65%. for the highest mAP, namely 2000 

iterations up to 6000 iterations, the maximum result is 100%. 

   In Figure 3, it can be explained that the detection of Malacca woven fabric motifs using YOLOv4 

has been successful and can detect the types of Malacca woven fabric motifs according to each class. 

The tests carried out to detect YOLOv4 objects are also very short and efficient in terms of time and 

accuracy. Therefore, the YOLOv4 method is very effective in detecting various motifs of Malacca 



Vol.1, No.2 | 49  

 

woven fabric. The results obtained by the YOLOv4 method can help the outside community to 

recognize Malacca woven fabric motifs. 

   In table II there is a comparison between the methods that have been used to detect Malacca 

woven fabric motifs with the proposed method. Previous research identified woven fabric motifs using 

the Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) methods. This 

research used 3 images of TTS tribal woven fabric with 2 methods studied. The results of this research 

were the highest, namely 91.67% in identifying TTS woven fabric using the Gray Level Coocrrence 

Matrix method. This result is relatively high, but in this case the research only used data on 3 images 

of TTS tribal woven fabric. The data is said to be very small because of the large number of woven 

fabrics. From the proposed goal, the researchers used Malacca woven fabric motifs using 1500 data 

with 4 types of classes for the process of detecting Malacca woven fabric motifs. The testing process 

uses 1500 Motig image data of Malacca woven fabric. The results obtained from this test were higher 

than the previous method, namely 100% detected using the YOLOv4 method on Malacca woven fabric 

motifs. 

 

IV. Conclusions 

The results of detecting Malacca woven fabric motifs using the YOLOv4 method prove that 

detecting Malacca woven fabric motifs according to the maximum class of Malacca woven fabric 

motifs, namely men's futus motifs, and the 1000th iteration produces an mAP level of 97.65%. And 

produced high mAP in the 4 classes of Malacca woven fabric motifs from 2000 iterations to 6000 

iterations with the highest mAP of 100%. This result is the very best result. 

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