ULOS FABRIC CLASSIFICATION USING ANDROID-BASED CONVOLUTIONAL NEURAL NETWORK

被引：3

作者：

Siregar, Arif Fadly ^{[1
]}

Mauritsius, Tuga ^{[1
]}

机构：

[1] Bina Nusantara Univ, BINUS Grad Program, Informat Syst Management Dept, JL KH Syandan 9, Jakarta 11480, Indonesia

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2021年 / 17卷 / 03期

关键词：

Ulos fabric; Batak tribe; Convolutional neural network; Machine learning; Deep learning; Android;

D O I：

10.24507/ijicic.17.03.753

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Indonesia is a country with diverse ethnic, religious, and cultural backgrounds. Among the tribes in Indonesia, one of them is the Batak tribe. The Batak tribe has a variety of cultures, one of which is ulos fabric. Every ulos fabric has meaning, and its patterns also have different meanings. However, in today's world, ulos fabric has begun to be forgotten. Many of ulos models and types are circulating, yet people can barely recognize them and sometimes they even do not know that it is a ulos fabric pattern. So to preserve the culture of this ulos fabric, we tried to classify the ulos fabric using the convolutional neural network method. We selected Convolutional Neural Network (CNN) because it shows better results in image recognition in recent years. We get the accuracy of around 87.27% in different factors. The model is then deployed to Application Programming Interface (API) to be used in android application that can predict the ulos fabric. The aim of the application and research is to help the people to recognize the ulos fabric pattern by taking pictures of it and then they will get information about the function of the ulos fabrics and its history that lies behind it.

引用

下载

页码：753 / 766

页数：14

共 50 条

[31] Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network
Feng, Kai
Pi, Xitian
Liu, Hongying
Sun, Kai
APPLIED SCIENCES-BASEL, 2019, 9 (09):
[32] Android Malware Detection Methods Based on Convolutional Neural Network: A Survey
Shu, Longhui
Dong, Shi
Su, Huadong
Huang, Junjie
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2023, 7 (05): : 1330 - 1350
[33] Classification of stroke disease using convolutional neural network
Marbun, J. T.
Seniman
Andayani, U.
2ND INTERNATIONAL CONFERENCE ON COMPUTING AND APPLIED INFORMATICS 2017, 2018, 978
[34] The Classification of Hypertensive Retinopathy using Convolutional Neural Network
Triwijoyo, Bambang Krismono
Budiharto, Widodo
Abdurachman, Edi
DISCOVERY AND INNOVATION OF COMPUTER SCIENCE TECHNOLOGY IN ARTIFICIAL INTELLIGENCE ERA, 2017, 116 : 166 - 173
[35] Automatic Document Classification Using Convolutional Neural Network
Sun, Xingping
Li, Yibing
Kang, Hongwei
Shen, Yong
2018 INTERNATIONAL SEMINAR ON COMPUTER SCIENCE AND ENGINEERING TECHNOLOGY (SCSET 2018), 2019, 1176
[36] Wetland Classification Using Deep Convolutional Neural Network
Mandianpari, Masoud
Rezaee, Mohammad
Zhang, Yun
Salehi, Bahram
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 9249 - 9252
[37] Sex Classification of Salmon Using Convolutional Neural Network
Kuramoto, Takumi
Abe, Shuji
Ishihata, Hiroaki
PROCEEDINGS OF THE 2020 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM), 2020,
[38] Classification of Nile Tilapia using Convolutional Neural Network
Hernandez, Rowell M.
Hernandez, Alexander A.
2019 IEEE 9TH INTERNATIONAL CONFERENCE ON SYSTEM ENGINEERING AND TECHNOLOGY (ICSET), 2019, : 126 - 131
[39] Advancements in Image Classification using Convolutional Neural Network
Sultana, Farhana
Sufian, Abu
Dutta, Paramartha
2018 FOURTH IEEE INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (ICRCICN), 2018, : 122 - 129
[40] Classification of Heart Sounds Using Convolutional Neural Network
Li, Fan
Tang, Hong
Shang, Shang
Mathiak, Klaus
Cong, Fengyu
APPLIED SCIENCES-BASEL, 2020, 10 (11):

← 1 2 3 4 5 →