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Performance evaluation of pre-trained deep learning model on garbage classification with data augmentation approach

IAES International Journal of Artificial Intelligence · Vol. 14 No. 6 (2025) · hlm. 4971–4981

DOI
10.11591/ijai.v14.i6.pp4971-4981
ISSN
2252-8938, 2089-4872
Terbit
2025-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Waste classification is one of the interesting topics for classifications in which data can be very varied and complex. This data diversity is a challenge to develop a model that is able to classify well. The purpose of this study is to analyze the performance of the pre-trained deep learning model using a data augmentation approach. There are three pre-training models used in this study, namely residual networks 50 (ResNet50), visual geometric group with 16 layers (VGG-16), and MobileNetV2. The results showed that the MobileNetV2 model received the highest accuracy value, reaching 84.45% for data without augmentation. With data augmentation there is a decrease of 2.73%. Conversely, VGG-16 shows performance stability with an increase in accuracy with augmentation data, reaching 75.84%. While ResNet50 gets the lowest results compared to both models. The application of data augmentation techniques with the aim of increasing data variations does not always have an impact on increasing the generalization of the model.

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  • Institut Bisnis dan Teknologi Indonesia
  • University of Nusa Cendana
  • Sebelas Maret University
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
I Made Aditya NugrahaAfiliasi tidak diketahui16
Putu SugiartawanInstitut Bisnis dan Teknologi Indonesia3
I Gede Iwan SudipaInstitut Bisnis dan Teknologi Indonesia3
Febi LuthfianiAfiliasi tidak diketahui3
Jhon Septin Mourisdo SiregarAfiliasi tidak diketahui2
Lebrina Ivantry BoikhUniversity of Nusa Cendana2
A A Gede Bagus ArianaAfiliasi tidak diketahui2
I Made Urip SumaharjaAfiliasi tidak diketahui2
I Putu Yudi PratamaInstitut Bisnis dan Teknologi Indonesia1
Grangsang SotyaramadhaniAfiliasi tidak diketahui1
Aris WidagdoAfiliasi tidak diketahui1
Anis KhairunnisaAfiliasi tidak diketahui1
Mahaldika CesranyAfiliasi tidak diketahui1
Muhamad Amril IdrusAfiliasi tidak diketahui1
I Wayan Dani PranataAfiliasi tidak diketahui1
Wawan HariantoAfiliasi tidak diketahui1
Anak Agung Surya PradhanaInstitut Bisnis dan Teknologi Indonesia1
I Nyoman Tri Anindia PutraAfiliasi tidak diketahui1
Sri WidiastutikAfiliasi tidak diketahui1
Slamet SlametSebelas Maret University1
Husaini UsmanAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 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">Performance evaluation of pre-trained deep learning model on garbage classification with data augmentation approach</dc:title>
	<dc:creator>Wiguna, I Komang Arya Ganda</dc:creator>
	<dc:creator>Desnanjaya, I Gusti Made Ngurah</dc:creator>
	<dc:creator>Sandika, I Kadek Budi</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Classification; Data augmentation; Deep learning; Garbage; Pre-trained model</dc:subject>
	<dc:description xml:lang="en-US">Waste classification is one of the interesting topics for classifications in which data can be very varied and complex. This data diversity is a challenge to develop a model that is able to classify well. The purpose of this study is to analyze the performance of the pre-trained deep learning model using a data augmentation approach. There are three pre-training models used in this study, namely residual networks 50 (ResNet50), visual geometric group with 16 layers (VGG-16), and MobileNetV2. The results showed that the MobileNetV2 model received the highest accuracy value, reaching 84.45% for data without augmentation. With data augmentation there is a decrease of 2.73%. Conversely, VGG-16 shows performance stability with an increase in accuracy with augmentation data, reaching 75.84%. While ResNet50 gets the lowest results compared to both models. The application of data augmentation techniques with the aim of increasing data variations does not always have an impact on increasing the generalization of the model.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-12-01</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27381</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i6.pp4971-4981</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 6: December 2025; 4971-4981</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i6</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27381/14906</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 I Komang Arya Ganda Wiguna, I Gusti Made Ngurah Desnanjaya, I Kadek Budi Sandika</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>