Skripsi

Komparasi Algoritme Support Vector Machine Dengan Linear Regression Untuk Diagnosis Penyakit Parkinson

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bibliografi: hal. 69

Abstrak

Parkinson's disease is a degenerative of central nervous system which causes thedistruptionof the nerve cells in the brain that will affect the movement of the sufferer. People affected by Parkinson's disease have low concentrations due to a lack of dopamine in the brain. Dopamine is a chemical in the body that serves as an introduction to a signal on the nerve. Early treatment using accurate method is needed in order to reduce the risk. A diagnosis method with data mining computation and machine learning is a good solution, in which data mining algorithm is used in classification of Parkinson's disease dataset. This study was aimed to compare of the performance result the Support vector machine (SVM) and linear regression algorithmin diagnosing Parkinson's disease. In order to increase the classification result, data mining algorithm can be combined with feature selection method.The comparison of both showed that, linear regression algorithmhad higher classification than SVM by accuracy point of 88,7% and 87,7%.Meanwhile, after feature selection with Particle Swarm Optimization is done, the result of algorithm obtained higher score at the point of linear regression algorithm at 90,6% and SVM algorithm at 88,7%.

Keywords: Parkinson's disease, diagnosis, data mining, classification.

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Ketersediaan

SI0869

SI 0869 KHA k

UPT. PERPUSTAKAAN (Rak 2)

Tersedia - No Loan

Informasi Detail

Judul Seri

-

No. Panggil

SI 0869 KHA k

Penerbit

Purwokerto : Universitas Amikom Purwokerto., 2016

Deskripsi Fisik

xviii, 68 hlm.: ilus.: lampiran.; 28 cm

Bahasa

Indonesia

ISBN/ISSN

12.12.0260

Klasifikasi

SI869

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

Oktober 2016

Subjek

-

Info Detail Spesifik

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Pernyataan Tanggungjawab

Qoim Khaironnuha

Versi lain/terkait

Lampiran Berkas

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