Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002186	T54203	T542032021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T542032021

Publisher

Inderalaya : Fakultas Ilmu Komputer, Prodi Teknik Informatika., 2021

Collation

xvi, 63 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)

Prodi Teknik Informatika
Pemrosesan Data, Ilmu Komputer, Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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