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Comparison of text-image fusion models for high school diploma certificate classification

Communications in Science and Technology · Vol. 5 No. 1 (2020) · hlm. 5–9

DOI
10.21924/cst.5.1.2020.172
ISSN
2502-9266, 2502-9258
Terbit
2020-07-02
Penerbit
Komunitas Ilmuwan dan Profesional Muslim Indonesia
Bahasa
eng

Abstrak

File scanned documents are commonly used in this digital era. Text and image extraction of scanned documents play an important role in acquiring information. A document may contain both texts and images. A combination of text-image classification has been previously investigated. The dataset used for those research works the text were digitally provided. In this research, we used a dataset of high school diploma certificate, which the text must be acquired using optical character recognition (OCR) method. There were two categories for this high school diploma certificate, each category has three classes. We used convolutional neural network for both text and image classifications. We then combined those two models by using adaptive fusion model and weight fusion model to find the best fusion model. We come into conclusion that the performance of weight fusion model which is 0.927 is better than that of adaptive fusion model with 0.892.

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37 penulis · 91 tautan kepenulisan bersama · 24 lintas organisasi

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  • Universitas Gadjah Mada
  • Telkom University
  • Muhammadiyah University Purwokerto
  • Universitas Islam Negeri Imam Bonjol Padang
  • Sunan Gunung Djati State Islamic University Bandung
  • State University of Semarang
  • Institut Teknologi Telkom Purwokerto
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Rekan penulisOrganisasiArtikel bersama
Sunu WibiramaUniversitas Gadjah Mada3
Hanung Adi NugrohoUniversitas Gadjah Mada3
Teguh Bharata AdjiUniversitas Gadjah Mada3
Oyas WahyunggoroUniversitas Gadjah Mada3
Widhia K.Z. OktoeberzaUniversitas Gadjah Mada3
Adha Imam CahyadiUniversitas Gadjah Mada2
Hendri Himawan TriharmintoUniversitas Gadjah Mada2
Achmad RizalTelkom University2
Risanuri HidayatUniversitas Gadjah Mada2
Lina ChoridahUniversitas Gadjah Mada2
Noor Akhmad SetiawanUniversitas Gadjah Mada2
Indah SoesantiUniversitas Gadjah Mada2
Aulia ArhamUniversitas Islam Negeri Imam Bonjol Padang2
Tri Putri LestariSunan Gunung Djati State Islamic University Bandung2
Rezty Amalia ArasUniversitas Gadjah Mada2
Etik IrijantiMuhammadiyah University of Yogyakarta2
Willy Anugrah CahyadiTelkom University1
Hasnely HasnelyAfiliasi tidak diketahui1
Budi WindartaUniversitas Gadjah Mada1
Anan NugrohoState University of Semarang1
Latifah ListyalinaUniversitas Gadjah Mada1
Christianus Frederick HotamaUniversitas Gadjah Mada1
Herryawan PujiharsonoInstitut Teknologi Telkom Purwokerto1
Faza Maula AzifUniversitas Gadjah Mada1
Febry Putra RochimUniversitas Gadjah Mada1
Vesi YulyantiUniversitas Gadjah Mada1
Dian Nova Kusuma HardaniMuhammadiyah University Purwokerto1
Aqil AqthobirrobbanyUniversitas Gadjah Mada1
Dian Nova Kusuma HardaniMuhammadiyah University Purwokerto1
Yenny RahmawatiUniversitas Gadjah Mada1
Kuntpong WoraratpanyaKing Mongkut's Institute of Technology Ladkrabang1
Syukron Abu Ishaq AlfaroziUniversitas Gadjah Mada1
Fauziazzuhry RahadianUniversitas Gadjah Mada1
Ratna Lestari Budiani BuanaUniversitas Gadjah Mada1

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Sumber metadata: OAI-PMH (metadata jurnal) https://cst.kipmi.or.id/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Comparison of text-image fusion models for high school diploma certificate classification</dc:title>
	<dc:creator>Atmaja Perdana, Chandra Ramadhan</dc:creator>
	<dc:creator>Adi Nugroho, Hanung</dc:creator>
	<dc:creator>Ardiyanto, Igi</dc:creator>
	<dc:subject xml:lang="en-US">text classification</dc:subject>
	<dc:subject xml:lang="en-US">image classification</dc:subject>
	<dc:subject xml:lang="en-US">text-image classification</dc:subject>
	<dc:subject xml:lang="en-US">convolutional neural network</dc:subject>
	<dc:description xml:lang="en-US">File scanned documents are commonly used in this digital era. Text and image extraction of scanned documents play an important role in acquiring information. A document may contain both texts and images. A combination of text-image classification has been previously investigated. The dataset used for those research works the text were digitally provided. In this research, we used a dataset of high school diploma certificate, which the text must be acquired using optical character recognition (OCR) method. There were two categories for this high school diploma certificate, each category has three classes. We used convolutional neural network for both text and image classifications. We then combined those two models by using adaptive fusion model and weight fusion model to find the best fusion model. We come into conclusion that the performance of weight fusion model which is 0.927 is better than that of adaptive fusion model with 0.892.</dc:description>
	<dc:publisher xml:lang="en-US">Komunitas Ilmuwan dan Profesional Muslim Indonesia</dc:publisher>
	<dc:date>2020-07-02</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://cst.kipmi.or.id/journal/article/view/172</dc:identifier>
	<dc:identifier>10.21924/cst.5.1.2020.172</dc:identifier>
	<dc:source xml:lang="en-US">Communications in Science and Technology; Vol. 5 No. 1 (2020); 5-9</dc:source>
	<dc:source xml:lang="id-ID">Communications in Science and Technology; Vol 5 No 1 (2020); 5-9</dc:source>
	<dc:source>2502-9266</dc:source>
	<dc:source>2502-9258</dc:source>
	<dc:source>10.21924/cst.5.1.2020</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://cst.kipmi.or.id/journal/article/view/172/64</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Communications in Science and Technology</dc:rights>
</oai_dc:dc>