Klasifikasi Penderita Penyakit Gagal Jantung Menggunakan Metode Support Vector Machine (SVM)

Dwi Febriansyah, . (2023) Klasifikasi Penderita Penyakit Gagal Jantung Menggunakan Metode Support Vector Machine (SVM). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

One of the cardiovascular diseases that is very dangerous to health and affects many people around the world is heart failure. Decisions about appropriate medical treatment can be made by detecting and classifying heart failure sufferers early. This research aims to use the Support Vector Machine (SVM) method to determine who has heart failure. The SVM method creates a hyperplane separating the two data classes with maximum margins. It is one of the most common and effective machine-learning methods for data classification. SVM can predict heart failure by studying patterns from relevant medical data, this is used in the classification of heart failure. Keywords: heart failure, support vector machine, classification, machine learning

Item Type:	Thesis (Skripsi)
Additional Information:	[No.Panggil: 1910511015] [Pembimbing: Didit Widiyanto] [Penguji 1: Bambang Saras Yulistiawan] [Penguji 2: Hengki Bayu Seta
Uncontrolled Keywords:	One of the cardiovascular diseases that is very dangerous to health and affects many people around the world is heart failure. Decisions about appropriate medical treatment can be made by detecting and classifying heart failure sufferers early. This research aims to use the Support Vector Machine (SVM) method to determine who has heart failure. The SVM method creates a hyperplane separating the two data classes with maximum margins. It is one of the most common and effective machine-learning methods for data classification. SVM can predict heart failure by studying patterns from relevant medical data, this is used in the classification of heart failure. Keywords: heart failure, support vector machine, classification, machine learning
Subjects:	Q Science > Q Science (General) R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine
Divisions:	Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User:	Dwi Febriansyah
Date Deposited:	23 Aug 2023 02:32
Last Modified:	23 Aug 2023 02:32
URI:	http://repository.upnvj.ac.id/id/eprint/26179

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