Skripsi

OPTIMASI METODE C4.5 MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI PENYAKIT DEMENSIA

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Penilaian

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The C4.5 method has weaknesses including problems when the dataset has large dimensions and attributes in the dataset are not all relevant attributes to the classification. These irrelevant attributes affect the accuracy of the C4.5 method. Therefore, optimization is needed to overcome these weaknesses. This study uses particle swarm optimization (PSO) to optimize the C4.5 method used for attribute selection by attribute weighting. The data used are medical records of people with dementia with 373 data. Based on the results of the study that the PSO method can improve the accuracy of the C4.5 method for the classification of dementia. The optimal parameter values obtained from the configuration experiment are the number of particles = 10 and the number of iterations = 75. The accuracy value obtained in the C4.5-PSO classification method is 91.93% and the increase in accuracy of the C4.5 method is 2.68%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002681	T54153	T541532021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T541532021

Publisher

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

Collation

xv, 81 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Pemrosesan Data, Teknik Informatika
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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