COMPARATIVE ANALYSIS OF SUPPORT VECTOR MACHINE (SVM) AND K-NEAREST NEIGHBOR (KNN) FOR TRAUMATIC BRAIN INJURY (TBI) CLASSIFICATION BASED ON ELECTROENCEPHALOGRAM (EEG)

DAMAYANTI, EMY (2023) COMPARATIVE ANALYSIS OF SUPPORT VECTOR MACHINE (SVM) AND K-NEAREST NEIGHBOR (KNN) FOR TRAUMATIC BRAIN INJURY (TBI) CLASSIFICATION BASED ON ELECTROENCEPHALOGRAM (EEG). Skripsi thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.

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Abstract

Traumatic brain injury (TBI) is the leading cause of death in the United States. Traumatic brain injury is indicated by the presence of various comorbidities, including neurological deficits, neuropsychiatric abnormalities, and cognitive impairment. Traumatic Brain Injury or TBI is defined as a functional impairment of the brain caused by an impact that leads to decreased consciousness, and brain impairment in the sufferer. This research can solve the problem by creating a prediction system for Electroencephalogram (EEG) results using Machine Learning. In this research, researchers will focus on analyzing the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms with EEG as a dataset for TBI.

Item Type:	Thesis (Skripsi)
Subjects:	000 Computer Science, Information and General Works
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	Mr Yosua Norman Rumondor
Date Deposited:	16 Apr 2024 01:37
Last Modified:	16 Apr 2024 01:37
URI:	http://repository.unika.ac.id/id/eprint/35165

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