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Deteksi Penyakit Dengue Hemorrhagic Fever dengan Pendekatan One Class Classification

Journal of Information Systems Engineering and Business Intelligence · Vol. 3 No. 2 (2017) · pp. 129–133

DOI
10.20473/jisebi.3.2.129-133
ISSN
2443-2555, 2598-6333
Published
2017-10-28
Publisher
Universitas Airlangga
Language
eng

Abstract

Abstrak” Pada kasus deteksi penderita penyakit demam berdarah (Dengue Hemorrhagic Fever- DHF), data training yang tersedia umumnya hanya data pasien penderita positif. Sedangkan data orang normal (data negatif) tidak tersedia secara khusus. Pada makalah ini dipaparkan pembangunan model klasifikasi untuk deteksi DHF dengan pendekatan One Class Classification (OCC). Data yang digunakan pada penelitian ini adalah hasil uji darah dari laboratorium dari pasien penderita penyakit demam berdarah. Metode yang diteliti adalah One-class Support Vector Machine dan K-Means. Hasil yang diperoleh pada penelitian ini adalah untuk metode SVM memiliki nilai precision = 1,0, recall = 0,993, f-1 score = 0,997, dan tingkat akurasi sebesar 99,7% sedangkan dengan metode K-Means diperoleh nilai precision = 0,901, recall = 0,973, f-1 score = 0,936, dan tingkat akurasi sebesar 93,3%. Hal ini menunjukkan bahwa metode SVM sedikit lebih unggul dibandingkan dengan K-Means untuk kasus ini. Kata Kunci” demam berdarah, Dengue Hemorrhagic Fever, K-Means, One Class Classification, OSVMAbstract” Two class classification problem maps input into two target classes. In certain cases, training data is available only in the form of a single class, as in the case of Dengue Hemorrhagic Fever (DHF) patients, where only data of positive patients is available. In this paper, we report our experiment in building a classification model for detecting DHF infection using One Class Classification (OCC) approach. Data from this study is sourced from laboratory tests of patients with dengue fever. The OCC methods compared are One-Class Support Vector Machine and One-Class K-Means. The result shows SVM method obtained precision value = 1.0, recall = 0.993, f-1 score = 0.997, and accuracy of 99.7% while the K-Means method obtained precision value = 0.901, recall = 0.973, f- 1 score = 0.936, and accuracy of 93.3%. This indicates that the SVM method is slightly superior to K-Means for One-Class Classification of DHF patients. Keywords” Dengue Hemorrhagic Fever, K-Means, One Class Classification, OSVM

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links

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  • Lambung Mangkurat University
  • Affiliation unknown
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Irwan BudimanLambung Mangkurat University1

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://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Deteksi Penyakit Dengue Hemorrhagic Fever dengan Pendekatan One Class Classification</dc:title>
	<dc:creator>Azkiya, Zida Ziyan</dc:creator>
	<dc:creator>Indriani, Fatma</dc:creator>
	<dc:creator>Chandra, Heru Kartika</dc:creator>
	<dc:subject xml:lang="en-US">Dengue Hemorrhagic Fever</dc:subject>
	<dc:subject xml:lang="en-US">K-Means</dc:subject>
	<dc:subject xml:lang="en-US">One Class Classification</dc:subject>
	<dc:subject xml:lang="en-US">OSVM</dc:subject>
	<dc:description xml:lang="en-US">Abstrak” Pada kasus deteksi penderita penyakit demam berdarah (Dengue Hemorrhagic Fever- DHF), data training yang tersedia umumnya hanya data pasien penderita positif. Sedangkan data orang normal (data negatif) tidak tersedia secara khusus. Pada makalah ini dipaparkan pembangunan model klasifikasi untuk deteksi DHF dengan pendekatan One Class Classification (OCC). Data yang digunakan pada penelitian ini adalah hasil uji darah dari laboratorium dari pasien penderita penyakit demam berdarah. Metode yang diteliti adalah One-class Support Vector Machine dan K-Means. Hasil yang diperoleh pada penelitian ini adalah untuk metode SVM memiliki nilai precision = 1,0, recall = 0,993, f-1 score = 0,997, dan tingkat akurasi sebesar 99,7%  sedangkan dengan metode K-Means diperoleh nilai precision = 0,901, recall = 0,973, f-1 score = 0,936, dan tingkat akurasi sebesar 93,3%. Hal ini  menunjukkan bahwa metode SVM sedikit lebih unggul dibandingkan dengan K-Means untuk kasus ini. Kata Kunci” demam berdarah, Dengue Hemorrhagic Fever, K-Means, One Class Classification, OSVMAbstract” Two class classification problem maps input into two target classes. In certain cases, training data is available only in the form of a single class, as in the case of Dengue Hemorrhagic Fever (DHF) patients, where only data of positive patients is available. In this paper, we report our experiment in building a classification model for detecting DHF infection using One Class Classification (OCC) approach. Data from this study is sourced from laboratory tests of patients with dengue fever. The OCC methods compared are One-Class Support Vector Machine and One-Class K-Means. The result shows SVM method obtained precision value = 1.0, recall = 0.993, f-1 score = 0.997, and accuracy of 99.7% while the K-Means method obtained precision value = 0.901, recall = 0.973, f- 1 score = 0.936, and accuracy of 93.3%. This indicates that the SVM method is slightly superior to K-Means for One-Class Classification of DHF patients. Keywords” Dengue Hemorrhagic Fever, K-Means, One Class Classification, OSVM</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2017-10-28</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://e-journal.unair.ac.id/JISEBI/article/view/5675</dc:identifier>
	<dc:identifier>10.20473/jisebi.3.2.129-133</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 3 No. 2 (2017): October; 129-133</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/5675/3899</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 Zida Ziyan Azkiya, Fatma Indriani, Heru Kartika Chandra</dc:rights>
</oai_dc:dc>