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Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 13 No. 1 (2020) · pp. 9–15

DOI
10.21609/jiki.v13i1.761
ISSN
2502-9274, 2088-7051
Published
2020-03-14
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.

Keywords

Collaboration network of the authors

7 authors · 13 co-authorship links

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  • Universitas Pertamina
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Sara Florensia TelaumbanuaUniversitas Pertamina1
Ade IrawanUniversitas Pertamina1

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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System</dc:title>
	<dc:creator>Nugroho, Herminarto</dc:creator>
	<dc:creator>Susanty, Meredita</dc:creator>
	<dc:creator>Irawan, Ade</dc:creator>
	<dc:creator>Koyimatu, Muhamad</dc:creator>
	<dc:creator>Yunita, Ariana</dc:creator>
	<dc:subject xml:lang="en-US">variational autoencoder</dc:subject>
	<dc:subject xml:lang="en-US">feature extraction</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">computer vision</dc:subject>
	<dc:subject xml:lang="en-US">fire detection system</dc:subject>
	<dc:description xml:lang="en-US">This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2020-03-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/761</dc:identifier>
	<dc:identifier>10.21609/jiki.v13i1.761</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 13 No. 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 9-15</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v13i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/761/420</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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