OPTIMASI HASIL KLASIFIKASI WAKTU TUNGGU KERJA DENGAN ALGORITMA SUPPORT VECTOR MACHINE MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION (PSO)

AMELIA, AMELIA and Utami, Alvi Syahrini and Kurniati, Rizki (2021) OPTIMASI HASIL KLASIFIKASI WAKTU TUNGGU KERJA DENGAN ALGORITMA SUPPORT VECTOR MACHINE MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION (PSO). Undergraduate thesis, Sriwijaya University.

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Abstract

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 work lay time Computer Science Faculty Sriwijaya University alumni that graduated in 2018 with a total number 240 data. Classification using Support Vector Machine algorithm resulted accuracy is 61,67%. While, features selection Particle Swarm Optimization on Support Vector Machine resulted average accuracy is 83,42%. The increase in average classification accuracy reaches 21,75%. Features selection Particle Swarm Optimization succeeded in increasing the accuracy of the Support Vector Machine algorithm in classifying data of work lay time.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification, Features Selection, Particle Swarm Optimization, Support Vector Machine, Work Lay Time.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Amelia Amelia
Date Deposited:	20 Sep 2021 01:54
Last Modified:	20 Sep 2021 01:54
URI:	http://repository.unsri.ac.id/id/eprint/54203

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