KLASIFIKASI PENYAKIT ALZHEIMER MENGGUNAKAN ALGORITMA GRADIENT BOOSTING

ROMDONA, BELHI and Utami, Alvi Syahrini (2022) KLASIFIKASI PENYAKIT ALZHEIMER MENGGUNAKAN ALGORITMA GRADIENT BOOSTING. Undergraduate thesis, Sriwijaya University.

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Abstract

Alzheimer's disease is a chronic, progressive neurological disease that impairs memory and other important memory functions and causes short-term memory loss and paranoia that is mistakenly attributed to stress or aging. Alzheimer's disease is caused by damage to nerve cells and connections in the brain. Although alzheimer's disease and other dementias are classified as untreatable, some symptoms such as infections, metabolic disorders, brain tumors, anoxia, etc. are considered curable with appropriate treatment. Therefore, a fast and simple system that is able to identify the presence of alzheimer's disease and its severity by utilizing a person's clinical and demographic data can be effective in providing a quick diagnosis. Based on previous studies, the Gradient Boosting algorithm has good accuracy so that in this research the classification of alzheimer's disease using the Gradient Boosting algorithm into 4 classes based on severity. Testing the classification performance using Confusion Matrix provides an average accuracy value of 87.50%, an average precision value of 87.45%, an average recall value of 87.49% and an average f1-score value of 87.54% from the application of the Gradient Boosting algorithm in classifying the level of alzheimer's disease.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Confusion Matrix, Gradient Boosting, Penyakit alzheimer
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Mr. Belhi Romdona
Date Deposited:	24 Nov 2022 03:39
Last Modified:	24 Nov 2022 03:39
URI:	http://repository.unsri.ac.id/id/eprint/82597

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