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Perbandingan Metode SVM, FUZZY-KNN, Dan BDT-SVM Untuk Klasifikasi Detak Jantung Hasil Elektrokardiografi

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 3 No. 3 (2016) · pp. 201–207

DOI
10.25126/jtiik.201633196
ISSN
2528-6579, 2355-7699
Published
2016-09-08
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakPerkembangan Ilmu Pengetahuan dan Teknologi (IPTEK) semakin pesat terutama dalam bidang kesehatan. Elektrokardiografi (EKG) merupakan salah satu metode untuk menentukan kondisi jantung manusia yang direpresentasikan dalam bentuk gelombang. Jantung merupakan organ vital manusia dan merupakan pertahanan hidup terakhir manusia selain otak. Di Indonesia berdasarkan data dari Kementrian RI tahun 2013, kematian akibat penyakit jantung ini diperkirakan sebesar 0.5% atau 883.447 orang dan berdasarkan gejala sebanyak 1.5% atau sekitar 2.650.340 orang. Kelas dari klasifikasi ini terdiri dari normal dan aritmia. Dimana aritmia terdiri dari atrial fibrillation, PVC bigeminy, dan ventricular tachycardia. Data didapatkan dari MIT-BIH Arrhytmia Database. Penelitian ini bertujuan untuk menentukan klasifikasi dari sinyal gelombang EKG tersebut dengan membandingkan metode Support Vector Machine dengan strategi One Against All, Fuzzy K-Nearest Neighbor, dan menggunakan metode Binary Decision Tree - Support Vector Machine. Hasil klasifikasi yang didapatkan dengan metode SVM memiliki rata-rata akurasi sebesar 81.30% menggunakan dataset dari fitur 3601 MLII dengan kernel Polynomial, metode Fuzzy-KNN 81.25% menggunakan jarak Manhattan, dan BDT-SVM sebesar 70.00% menggunakan kernel Polynomial dengan menggunakan data sebesar 140 dataset.Kata Kunci: Support Vector Machine, Binary Decision Tree, Fuzzy-KNN, Detak Jantung, ElektrokardiografiAbstractThe development of Science and Technology growing rapidly, especially in the health field. Electrocardiography (ECG) is one method for determining the condition of the human heart rate that is represented in the form of waves. The human heart is a vital organ and is the last surviving human defense other than the brain. In Indonesia based on data from the Ministry of Indonesia in 2013, the death from heart disease is estimated about 0.5% or 883.447 people and is based on the symptoms as much as 1.5%, or about 2.65034 million people. Class of this study such as normal and arrhythmias. Arrhythmias consists of atrial fibrillation, PVC bigeminy, and ventricular tachycardia. Data collected from the MIT-BIH Arrhythmias Database. This study aims to determine the classification of the ECG waveform signal by comparing SVM algorithm is supported by One Against All of the strategies, Fuzzy K-Nearest Neighbor, and Binary Decision Tree - Support Vector Machine. The average results of classification using SVM have an accuracy of 81.30% by using a dataset from 3601 MLII features with the polynomial kernel, 81.25% with Fuzzy-KNN by Manhattan Distance, and 70.00% with BDT-SVM using polynomial kernel using data of 140 datasets.Keywords: Support Vector Machine, Binary Decision Tree, Fuzzy-KNN, Heart Rate, Electrocardiography

Collaboration network of the authors

40 authors · 108 co-authorship links · 3 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Universitas Amikom Purwokerto
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arief Andy SoebrotoUniversity of Brawijaya3
Daneswara JauhariUniversity of Brawijaya3
Dhimas Anjar PrabowoUniversity of Brawijaya3
Sutrisno .Affiliation unknown2
Nurul HidayatUniversity of Brawijaya2
Canny Amerilyse CaesarUniversity of Brawijaya2
Latifah HanumUniversity of Brawijaya2
Muhammad FhadliUniversity of Brawijaya2
Anang HanafiUniversity of Brawijaya2
Listiya SurtiningsihUniversity of Brawijaya2
Fitra Abdurrachman BachtiarUniversity of Brawijaya2
Ade NurhopipahUniversitas Amikom Purwokerto1
Ahmad Afif SupiantoUniversity of Brawijaya1
Randy Cahya WihandikaUniversity of Brawijaya1
Maria Tenika FrestantiyaUniversity of Brawijaya1
Ziya El AriefUniversity of Brawijaya1
Candra DewiUniversity of Brawijaya1
Rina ChristantiUniversity of Brawijaya1
Budi Darma SetiawanUniversity of Brawijaya1
Rekyan Regasari Mardi PutriUniversity of Brawijaya1
Lailil MuflikhahUniversity of Brawijaya1
Muhammad SyafiqUniversity of Brawijaya1
Achmad Jafar Al KadafiUniversity of Brawijaya1
Rizka Husnun ZakiyyahUniversity of Brawijaya1
Wanda Athira LuqyanaUniversity of Brawijaya1
Mochammad Ainun NajibUniversity of Brawijaya1
Handika Agus FauziUniversity of Brawijaya1
Sutrisno SutrisnoUniversity of Brawijaya1
Aryeswara SunaryoUniversity of Brawijaya1
Maryamah .University of Brawijaya1
Moh. Fadel AsikinUniversity of Brawijaya1
Daisy KurniawatyUniversity of Brawijaya1
Selly Kurnia SariUniversity of Brawijaya1
Agnes Rossi Trisna LestariUniversity of Brawijaya1
Umi RofiqohUniversity of Brawijaya1
Siti RobbanaUniversity of Brawijaya1

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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Perbandingan Metode SVM, FUZZY-KNN, Dan BDT-SVM Untuk Klasifikasi Detak Jantung Hasil Elektrokardiografi</dc:title>
	<dc:creator xml:lang="en">Hasanah, Uswatun</dc:creator>
	<dc:creator xml:lang="en">Mayangsari, Lintang Resita</dc:creator>
	<dc:creator xml:lang="en">Pratama, Andhica</dc:creator>
	<dc:creator xml:lang="en">Cholissodin, Imam</dc:creator>
	<dc:description xml:lang="id">AbstrakPerkembangan Ilmu Pengetahuan dan Teknologi (IPTEK) semakin pesat terutama dalam bidang kesehatan. Elektrokardiografi (EKG) merupakan salah satu metode untuk menentukan kondisi jantung manusia yang direpresentasikan dalam bentuk gelombang. Jantung merupakan organ vital manusia dan merupakan pertahanan hidup terakhir manusia selain otak. Di Indonesia berdasarkan data dari Kementrian RI tahun 2013, kematian akibat penyakit jantung ini diperkirakan sebesar 0.5% atau 883.447 orang dan berdasarkan gejala sebanyak 1.5% atau sekitar 2.650.340 orang. Kelas dari klasifikasi ini terdiri dari normal dan aritmia. Dimana aritmia terdiri dari atrial fibrillation, PVC bigeminy, dan ventricular tachycardia. Data didapatkan dari MIT-BIH Arrhytmia Database. Penelitian ini bertujuan untuk menentukan klasifikasi dari sinyal gelombang EKG tersebut dengan membandingkan metode Support Vector Machine dengan strategi One Against All, Fuzzy K-Nearest Neighbor, dan menggunakan metode Binary Decision Tree - Support Vector Machine. Hasil klasifikasi yang didapatkan dengan metode SVM memiliki rata-rata akurasi sebesar 81.30% menggunakan dataset dari fitur 3601 MLII dengan kernel Polynomial, metode Fuzzy-KNN 81.25% menggunakan jarak Manhattan, dan BDT-SVM sebesar 70.00% menggunakan kernel Polynomial dengan menggunakan data sebesar 140 dataset.Kata Kunci: Support Vector Machine,  Binary Decision Tree, Fuzzy-KNN, Detak Jantung, ElektrokardiografiAbstractThe development of Science and Technology growing rapidly, especially in the health field. Electrocardiography (ECG) is one method for determining the condition of the human heart rate that is represented in the form of waves. The human heart is a vital organ and is the last surviving human defense other than the brain. In Indonesia based on data from the Ministry of Indonesia in 2013 , the death from heart disease is estimated about 0.5% or 883.447 people and is based on the symptoms as much as 1.5%, or about 2.65034 million people. Class of this study such as normal and arrhythmias. Arrhythmias consists of atrial fibrillation, PVC bigeminy, and ventricular tachycardia. Data collected from the MIT-BIH Arrhythmias Database. This study aims to determine the classification of the ECG waveform signal by comparing SVM algorithm is supported by One Against All of the strategies, Fuzzy K-Nearest Neighbor, and Binary Decision Tree - Support Vector Machine. The average results of classification using SVM have an accuracy of 81.30% by using a dataset from 3601 MLII features with the polynomial kernel, 81.25% with Fuzzy-KNN by Manhattan Distance, and 70.00% with BDT-SVM using polynomial kernel using data of 140 datasets.Keywords: Support Vector Machine, Binary Decision Tree, Fuzzy-KNN, Heart Rate, Electrocardiography</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2016-09-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>10.25126/jtiik.201633196</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 3 No 3: September 2016; 201-207</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 3 No. 3: September 2016; 201-207</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201633</dc:source>
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	<dc:rights xml:lang="en">Copyright (c) 2016 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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