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Heart Disease Diagnosis Using Tsukamoto Fuzzy Method

Journal of Advances in Information Systems and Technology · Vol. 5 No. 1 (2023) · hlm. 12–22

DOI
10.15294/jaist.v5i1.67565
ISSN
2715-999X, 2714-9714
Terbit
2023-04-04
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

As one of the leading causes of death in the world, heart disease needs special attention. Heart disease often causes sudden death because the signs of a heart attack are not easy to detect. However, early detection efforts can still be pursued and continue to be carried out, especially using information technology. This study aims to diagnose the risk level of heart disease using Tsukamoto method and involving 11 input variables such as cholesterol, blood pressure, ECG, and others. At the same time, the output variables include healthy, small, medium, large, and very large. The stages of the method consist of four main processes, namely literature review, fuzzy inference system design, applying of Tsukamoto fuzzy, and evaluation. The research concluded that the fuzzy logic of the Tsukamoto method can be used to diagnose the risk level of heart disease, although the model performance is still limited to an accuracy value of 58%.

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21 penulis · 54 tautan kepenulisan bersama · 1 lintas organisasi

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  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Budi PrasetiyoState University of Semarang1
Reiki Aziz Yoga UtamaAfiliasi tidak diketahui1
Ahmad Bagas Aditya IlhamAfiliasi tidak diketahui1
Bambang Eko SusiloState University of Semarang1
Trida Ridho FarizState University of Semarang1
Sri SukaesihState University of Semarang1
Siti WahyuniAfiliasi tidak diketahui1
Aditya MariantiAfiliasi tidak diketahui1
Virgania SariAfiliasi tidak diketahui1
Nuni WidiartiAfiliasi tidak diketahui1
M. Fadil MardiansyahAfiliasi tidak diketahui1
Kholiq BudimanAfiliasi tidak diketahui1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Maylinna Rahayu NingsihAfiliasi tidak diketahui1
Yopi Julia NurriskiAfiliasi tidak diketahui1
Fathimah Az Zahra SanjaniAfiliasi tidak diketahui1
Jumanto UnjungAfiliasi tidak diketahui1
Wiyanda PuspitaAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Heart Disease Diagnosis Using Tsukamoto Fuzzy Method</dc:title>
	<dc:creator>Hakim, M. Faris Al</dc:creator>
	<dc:creator>Fajriati, Nafa</dc:creator>
	<dc:creator>Pratama, Rizka Nur</dc:creator>
	<dc:description xml:lang="en-US">As one of the leading causes of death in the world, heart disease needs special attention. Heart disease often causes sudden death because the signs of a heart attack are not easy to detect. &amp;nbsp;&amp;nbsp;However, early detection efforts can still be pursued and continue to be carried out, especially using information technology. This study aims to diagnose the risk level of heart disease using Tsukamoto method and involving 11 input variables such as cholesterol, blood pressure, ECG, and others. At the same time, the output variables include healthy, small, medium, large, and very large. The stages of the method consist of four main processes, namely literature review, fuzzy inference system design, applying of Tsukamoto fuzzy, and evaluation. The research concluded that the fuzzy logic of the Tsukamoto method can be used to diagnose the risk level of heart disease, although the model performance is still limited to an accuracy value of 58%.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2023-04-04</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/67565</dc:identifier>
	<dc:identifier>10.15294/jaist.v5i1.67565</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 5 No 1 (2023): April; 12-22</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v5i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/67565/24037</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>