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

DAMAYANTI, APRINA and Utami, Alvi Syahrini and Marieska, Mastura Diana (2023) OPTIMASI METODE SUPPORT VECTOR MACHINE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI PENYAKIT AUTISME. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Prediksi, Particle Swarm Optimization, Seleksi Fitur, Support Vector Machine, Penyakit Autisme
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Aprina Damayanti
Date Deposited:	27 Jan 2023 04:59
Last Modified:	27 Jan 2023 04:59
URI:	http://repository.unsri.ac.id/id/eprint/88090

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