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OPTIMASI METODE SUPPORT VECTOR MACHINE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI PENYAKIT AUTISME

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Penilaian

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Support Vector Machine is a method in machine learning that can be used to analyze data and sort it into one of two categories. Support Vector Machine has disadvantages in determining the optimal parameter and suitable features, this has an effect on the value of accuracy produced. Therefore, optimization is needed to select the features to be used. This study optimizes the Support Vector Machine algorithm with features selection using Particle Swarm Optimization. The data used is autism spectrum disorder with a total number 104 data. Prediction using Support Vector Machine algorithm resulted accuracy is 50%. While, features selection Particle Swarm Optimization on Support Vector Machine resulted average accuracy is 69%. The increase in average prediction accuracy reaches 19%. Features selection Particle Swarm Optimization succeeded in increasing the accuracy of the Support Vector Machine algorithm in predicting data of autism spectrum disorder.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307001014	T88090	T880902023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T880902023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

vi, 109 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004. 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
teknik komputer informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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