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Jurnal
SINTA 2
Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 2460-7258, 1978-1520
Published 2025-07-31
Publisher IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language eng
Abstract
This research aims to overcome general challenges in the field of image pattern recognition using a convolutional neural network (CNN), which is still faced with the complexity and limitations of image data. Achieving high accuracy is essential because it significantly influences the effectiveness and success of numerous areas. Although deep learning technology, especially CNNs, offers the potential to improve accuracy, it is still limited to the 70–80% range for achieving the expected level of accuracy. In this research, a fusion method was developed that combines pre-trained models using concatenation techniques to increase accuracy. By utilizing pre-trained models such as ResNet50, VGG16, and MobileNet-v2, which were then adapted to various datasets and cross-validation techniques, researchers managed to achieve significant improvements in accuracy. The results of this study show an improvement in the accuracy of the Fusion Multi-CNN model for various datasets. On the fashion dataset, MNIST managed to achieve an accuracy of 0.87840, while on CIFAR-10 and Oxford-102, the accuracy was 0.81260 and 0.84004, respectively.
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Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.
Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai,
first harvested 26 Okt 2026,
last seen 26 Okt 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en-US">Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method</dc:title>
<dc:creator>Hamda, Hizbullah</dc:creator>
<dc:creator>Wibowo, Moh Edi</dc:creator>
<dc:subject xml:lang="en-US">computer science; pattern recognition; image classification</dc:subject>
<dc:subject xml:lang="en-US">image classification; enhancement; feature fusion; convolutional neural network</dc:subject>
<dc:description xml:lang="en-US">This research aims to overcome general challenges in the field of image pattern recognition using a convolutional neural network (CNN), which is still faced with the complexity and limitations of image data. Achieving high accuracy is essential because it significantly influences the effectiveness and success of numerous areas. Although deep learning technology, especially CNNs, offers the potential to improve accuracy, it is still limited to the 70–80% range for achieving the expected level of accuracy. In this research, a fusion method was developed that combines pre-trained models using concatenation techniques to increase accuracy. By utilizing pre-trained models such as ResNet50, VGG16, and MobileNet-v2, which were then adapted to various datasets and cross-validation techniques, researchers managed to achieve significant improvements in accuracy. The results of this study show an improvement in the accuracy of the Fusion Multi-CNN model for various datasets. On the fashion dataset, MNIST managed to achieve an accuracy of 0.87840, while on CIFAR-10 and Oxford-102, the accuracy was 0.81260 and 0.84004, respectively.</dc:description>
<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2025-07-31</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://journal.ugm.ac.id/ijccs/article/view/98531</dc:identifier>
<dc:identifier>10.22146/ijccs.98531</dc:identifier>
<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 3 (2025): July; 235-246</dc:source>
<dc:source>2460-7258</dc:source>
<dc:source>1978-1520</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/98531/42102</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Citations and indexing
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Cite this article
APA 7
Hamda, H., & Wibowo, M. E. (2025). Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 19(3), 235–246. https://doi.org/10.22146/ijccs.98531
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IEEE
H. Hamda and M. E. Wibowo, “Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 19, no. 3, pp. 235–246, 2025, doi: 10.22146/ijccs.98531.
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