PERBANDINGAN ALGORTIMA KLASIFIKASI DECISION TREE C4.5 DAN SUPPORT VECTOR MACHINE (SVM) DALAM PREDIKSI PENDERITA STROKE BERBASIS PSO

Bhuana Lintang, Chindu and Widi Nugroho, Handoyo (2023) PERBANDINGAN ALGORTIMA KLASIFIKASI DECISION TREE C4.5 DAN SUPPORT VECTOR MACHINE (SVM) DALAM PREDIKSI PENDERITA STROKE BERBASIS PSO. Skripsi thesis, Institut Informatika dan Bisnis Darmajaya Bandar Lampung.

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A. JUDUL.pdf
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B. KEASLIAN, PERSETUJUAN, PENGESAHAN CETAK.pdf
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C. KATA PENGANTAR, MOTTO.pdf
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D. ABSTRAK.pdf
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E. DAFTAR ISI.pdf
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BAB I.pdf
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F. DAFTAR PUSTAKA.pdf
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Abstract

Abstract Stroke is a disease that attacks the brain in the form of an attack on local or global nerve function. In medical terms, it is usually called a Transient Ischemic Attack. Impaired nerve function in stroke is caused by non-traumatic brain blood circulation disorders. Handling stroke must be carried out quickly and precisely in order to avoid disability or further complications. Machine learning is a technology that can be used to predict stroke. Machine learning algorithms are constructive in making accurate predictions and providing accurate analysis. C4.5 Decision Tree Algorithm and Support Vector Machine (SVM) Algorithm. The aim of this study is to compare the accuracy and AUC values combined with Particle Sward Optimization (PSO) in these two algorithms to predict stroke sufferers. Based on the research results, it was found that the PSO-based Support Vector Machine algorithm obtained an accuracy value of 99.11% with an AUC value of 1,000 while the PSO-based Decision Tree C4.5 algorithm obtained an accuracy value of 96.56% with an AUC value of 0.952. Keywords: Decision tree C4.5, Support Vector Machine, Machine Learning, Stroke

Item Type: Thesis (Skripsi)
Subjects: 600 Teknologi - Ilmu terapan > 620 Ilmu Teknik - Engineering
eTheses
Divisions: Pasca Sarjana > Magister Teknik Informatika
Depositing User: Chindu Lintang Bhuana
Date Deposited: 26 Jun 2023 08:01
Last Modified: 26 Jun 2023 08:01
URI: http://repo.darmajaya.ac.id/id/eprint/12136

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