OPTIMASI ALGORITMA NAÏVE BAYES MENGGUNAKAN ANT COLONY OPTIMIZATION UNTUK KLASIFIKASI DATA PENDERITA PENYAKIT STROKE

MINARI, KURNIA and Rini, Dian Palupi (2023) OPTIMASI ALGORITMA NAÏVE BAYES MENGGUNAKAN ANT COLONY OPTIMIZATION UNTUK KLASIFIKASI DATA PENDERITA PENYAKIT STROKE. Undergraduate thesis, Sriwijaya University.

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Abstract

Stroke is a dangerous disease that is included in the 10 deadliest diseases in Indonesia, with a very high mortality rate. To detect strokes, CT scans and MRI examinations are usually used. However, this examination is expensive and is often only carried out after someone has had a stroke. As a result, medical treatment can be delayed and the patient's condition can worsen. Therefore, it is very important for a method that can identify a patient's condition at an early stage to detect the risk of stroke by classifying whether the patient is experiencing a stroke or not. One method that is often used for classification is Naïve Bayes. However, this method has the weakness of passing data into certain classes even though the data is not relevant or relevant so it needs to be optimized by feature selection. From this, optimization was carried out using the Ant Colony Optimization algorithm for feature selection for classification using Naïve Bayes. In this research, feature selection will be carried out on the data that will be classified with Naïve Bayes using the Ant Colony Optimization algorithm. The accuracy of the Naïve Bayes method before and after being optimized with the Ant Colony Optimization algorithm was 86.45% and 95.84% without the use of resampling techniques, while with the application of resampling techniques, the accuracy was 78.69% and 79.91%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Ant Colony Optimization, Feature Selection, Klasifikasi, Naïve Bayes, Stroke, Supervised Learning
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Kurnia Minari
Date Deposited:	05 Jan 2024 04:51
Last Modified:	05 Jan 2024 04:51
URI:	http://repository.unsri.ac.id/id/eprint/137557

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