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Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Sinergi · Vol. 27 No. 2 (2023) · hlm. 163–170

DOI
10.22441/sinergi.2023.2.003
ISSN
2460-1217, 1410-2331
Terbit
2023-04-15
Penerbit
Universitas Mercu Buana
Bahasa
eng

Abstrak

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.

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26 penulis · 63 tautan kepenulisan bersama · 36 lintas organisasi

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  • Mercu Buana University
  • Beijing Institute of Technology
  • Jakarta State Polytechnic
  • Universitas Esa Unggul
  • Kigali Independent University
  • National Defence University of Malaysia
  • Bandung Institute of Technology
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  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Trie Maya KadarinaMercu Buana University3
Arifiansyah ZodyUniversitas Esa Unggul2
Rachmat MuwardiMercu Buana University2
Eko IhsantoMercu Buana University1
Fina SupeginaMercu Buana University1
Muhammad Imam MuthahharMercu Buana University1
Asif KhanBeijing Institute of Technology1
Andi AdriansyahMercu Buana University1
Sabin HitimanaKigali Independent University1
Bugi Nur RohmanMercu Buana University1
Rahmat MuwardiMercu Buana University1
Asif KhanBeijing Institute of Technology1
Rinto PriambodoMercu Buana University1
Riandini RiandiniJakarta State Polytechnic1
Sulis SetiowatiJakarta State Polytechnic1
Rika Novita WardhaniJakarta State Polytechnic1
Mohd Taufik JusohNational Defence University of Malaysia1
Muhammad Fadhlillah Raisul AkbarMercu Buana University1
Muhammad Iqbal MaulanaMercu Buana University1
Ketty Siti SalamahMercu Buana University1
Arrival Dwi SentosaBandung Institute of Technology1

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Sumber metadata: OAI-PMH (metadata jurnal) https://publikasi.mercubuana.ac.id/index.php/sinergi/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture</dc:title>
	<dc:creator>Aziz, Khoirul</dc:creator>
	<dc:creator>Trisiawan, Inggis Kurnia</dc:creator>
	<dc:creator>Suyasmini, Kadek Dwi</dc:creator>
	<dc:creator>Iklima, Zendi</dc:creator>
	<dc:creator>Yunita, Mirna</dc:creator>
	<dc:subject xml:lang="en-US">Convolutional Neural Network (CNN)</dc:subject>
	<dc:subject xml:lang="en-US">Polyethylene Terephthalate Network (PETNet)</dc:subject>
	<dc:subject xml:lang="en-US">Quality Control</dc:subject>
	<dc:subject xml:lang="en-US">Transfer Learning Model</dc:subject>
	<dc:subject xml:lang="en-US">Visual Inspection</dc:subject>
	<dc:description xml:lang="en-US">Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Mercu Buana</dc:publisher>
	<dc:date>2023-04-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:format>application/msword</dc:format>
	<dc:identifier>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/15302</dc:identifier>
	<dc:identifier>10.22441/sinergi.2023.2.003</dc:identifier>
	<dc:source xml:lang="en-US">SINERGI; Vol. 27 No. 2 (2023); 163-170</dc:source>
	<dc:source>2460-1217</dc:source>
	<dc:source>1410-2331</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/15302/6812</dc:relation>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/15302/11288</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 SINERGI</dc:rights>
</oai_dc:dc>