KOMPARASI BINARY DRAGONFLY ALGORITHM DAN BINARY PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI STROKE DENGAN SUPPORT VECTOR MACHINE

SUPARMADI, FERENC JANOS (2025) KOMPARASI BINARY DRAGONFLY ALGORITHM DAN BINARY PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI STROKE DENGAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana Jakarta.

Preview	Text (HAL COVER) 01 COVER.pdf Download (533kB) | Preview
	Text (BAB I) 02 BAB 1.pdf Restricted to Registered users only Download (170kB)
	Text (BAB II) 03 BAB 2.pdf Restricted to Registered users only Download (453kB)
	Text (BAB III) 04 BAB 3.pdf Restricted to Registered users only Download (154kB)
	Text (BAB IV) 05 BAB 4.pdf Restricted to Registered users only Download (437kB)
	Text (BAB V) 06 BAB 5.pdf Restricted to Registered users only Download (30kB)
	Text (DAFTRA PUSTAKA) 07 DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (172kB)
	Text (LAMPIRAN) 08 LAMPIRAN.pdf Restricted to Registered users only Download (549kB)

Abstract

Stroke is a deadly disease that can take a life. Stroke is also the third leading cause of death globally after coronary heart disease and cancer. In the development of technology and information, especially in the field of machine learning, it is expected to play a role in making early predictions and become a useful step for treatment so as to help health workers in making clinical decisions. One way of machine learning to detect data matches from several symptoms or causal factors is algorithm performance testing. This research uses a combined method between optimization algorithms and classification algorithms by comparing the accuracy of the results obtained from the Binary Dragonfly Algorithm (BDA) and Binary Particle Swarm Optimization (BPSO) optimization algorithms based on Support Vector Machine (SVM) classification algorithm. This research provides a new contribution to academic knowledge in the field of medical data from the use of these algorithms and provides guidelines or methodologies used in stroke disease prediction data classification research. This research produces performance accuracy values of Binary Dragonfly Algorithm (BDA) and Binary Particle Swarm Optimization (BPSO) based on Support Vector Machine (SVM) in predicting stroke datasets. Keyword : Stroke, Binary Dragonfly Algorithm, Binary Particle Swarm Optimization, Support Vector Machine. Stroke adalah penyakit berbahaya yang bisa merenggut nyawa manusia. Stroke juga merupakan penyakit penyebab kematian nomor tiga secara global setelah penyakit jantung koroner dan kanker. Dalam berkembangnya teknologi dan informasi khususnya di bidang machine learning, diharapkan memberikan peran dalam melakukan prediksi dini dan menjadi langkah yang berguna untuk pengobatan sehingga membantu tenaga kesehatan dalam membuat keputusan klinis. Salah satu cara machine learning untuk mendeteksi kecocokan data dari beberapa gejala atau faktor penyebab yaitu pengujian kinerja algoritma. Penelitian ini menggunakan metode gabungan antara algoritma optimisasi dan algoritma klasifikasi dengan membandingkan tingkat akurasi hasil yang diperoleh dari algoritma optimisasi Binary Dragonfly Algorithm (BDA) dan Binary Particle Swarm Optimization (BPSO) yang dilatih kembali oleh algoritma klasifikasi Support Vector Machine (SVM). Penelitian ini memberikan kontribusi baru terhadap pengetahuan akademis dalam bidang data medis dari penggunaan algoritma tersebut dan memberikan panduan atau metodologi yang digunakan dalam penelitian klasifikasi prediksi data penyakit stroke. Penelitian ini menghasilkan nilai akurasi kinerja dari Binary Dragonfly Algorithm (BDA) dan Binary Particle Swarm Optimization (BPSO) dengan Support Vector Machine (SVM) dalam memprediksi dataset stroke. Kata Kunci : Stroke, Binary Dragonfly Algorithm, Binary Particle Swarm Optimization, Support Vector Machine

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 076
NIM/NIDN Creators:	41821010057
Uncontrolled Keywords:	Stroke, Binary Dragonfly Algorithm, Binary Particle Swarm Optimization, Support Vector Machine
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.7 Data in Computer Systems/Data dalam Sistem-sistem Komputer > 005.74 Data Files and Database/Data File-file dan Database, Pangkalan Data, Pusat Data 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.7 Data in Computer Systems/Data dalam Sistem-sistem Komputer > 005.75 Specific Types of Data Files and Databases/Jenis Spesifik File Data dan Pangakalan Data > 005.754 Network Databases/Pangakalan Data Jaringan 600 Technology/Teknologi > 610 Medical, Medicine, and Health Sciences/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan > 615 Pharmacology and Therapeutics/Farmakologi dan Terapi Farmakologi > 615.1 Drugs, Medicine, Medical Material/Obat-obatan, Peralatan Medis 600 Technology/Teknologi > 610 Medical, Medicine, and Health Sciences/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan > 616 Diseases/Penyakit > 616.1 Diseases of Cardiovascular System/Penyakit pada Sistem Kardiovaskular
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	25 Aug 2025 03:16
Last Modified:	25 Aug 2025 03:16
URI:	http://repository.mercubuana.ac.id/id/eprint/97058

Actions (login required)

View Item