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AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 12 No. 2 (2019) · pp. 57–66

DOI
10.21609/jiki.v12i2.642
ISSN
2502-9274, 2088-7051
Published
2019-07-08
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Dengue hemorrhagic fever is one of the most dangerous diseases which often leads to death for the sufferer due to delays or improper handling of the severity that has occurred. In determining that severity level, a specialist analyzes it from the symptoms and blood testing results. This research was developed to produce a system by applying Deep Neural Network approach that is able to give the same analytical ability as a doctor, so that it can give fast and precise decision of dengue handling. The research stages consisted of normalizing data to 0 – 1 intervals by Min-Max method, training data into multilayer networks with fully connected and partially connected schemes to produce the best weights, validating data and final testing. From the use of network parameters as much as 10 input units, 1 bias, 2 hidden layers, 2 output units, learning rate of 0.3, epoch 1000, tolerance rate 0.02, threshold 0.5, the system succeeded in generating a maximum accuracy of 95% in data learning (60 data), 87.5% on data learning and non-learning (40 data), 85% on non-learning data (20 data).

Keywords

Collaboration network of the authors

7 authors · 11 co-authorship links

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  • Universitas Trisakti
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Fiqih Hana SaputriUniversitas Trisakti2
Nurizka KhoeraniAffiliation unknown1
Syandra SariAffiliation unknown1

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 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH</dc:title>
	<dc:creator>Pratiwi, Dian</dc:creator>
	<dc:creator>Santoso, Gatot Budi</dc:creator>
	<dc:creator>Muslimah, Leni</dc:creator>
	<dc:creator>Rizki, Raden Davin</dc:creator>
	<dc:subject xml:lang="en-US">blood</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">dengue</dc:subject>
	<dc:subject xml:lang="en-US">dropout</dc:subject>
	<dc:subject xml:lang="en-US">min-max norm</dc:subject>
	<dc:description xml:lang="en-US">Dengue hemorrhagic fever is one of the most dangerous diseases which often leads to death for the sufferer due to delays or improper handling of the severity that has occurred. In determining that severity level, a specialist analyzes it from the symptoms and blood testing results. This research was developed to produce a system by applying Deep Neural Network approach that is able to give the same analytical ability as a doctor, so that it can give fast and precise decision of dengue handling. The research stages consisted of normalizing data to 0 – 1 intervals by Min-Max method, training data into multilayer networks with fully connected and partially connected schemes to produce the best weights, validating data and final testing. From the use of network parameters as much as 10 input units, 1 bias, 2 hidden layers, 2 output units, learning rate of 0.3, epoch 1000, tolerance rate 0.02, threshold 0.5, the system succeeded in generating a maximum accuracy of 95% in data learning (60 data), 87.5% on data learning and non-learning (40 data), 85% on non-learning data (20 data).</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2019-07-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/642</dc:identifier>
	<dc:identifier>10.21609/jiki.v12i2.642</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 12 No. 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 57-66</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v12i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/642/412</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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