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

WIANTARA, TUBAGUS and Samsuryadi, Samsuryadi (2021) OPTIMASI METODE C4.5 MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI PENYAKIT DEMENSIA. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Attribute Selection, Attribute Weighting, C4.5, Particle Swarm Optimization, Dementia Disease.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Tubagus Wiantara
Date Deposited:	20 Sep 2021 08:23
Last Modified:	20 Sep 2021 08:23
URI:	http://repository.unsri.ac.id/id/eprint/54153

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