Skripsi

PENGEMBANGAN SISTEM KLASIFIKASI PENYAKIT ALZHEIMER PADA CITRA MAGNETIC RESONANCE IMAGING (MRI) MENGGUNAKAN INCEPTIONNET

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Alzheimer's disease is a slowly progressing neurodegenerative disorder characterized by memory decline, visual-spatial impairment, executive function impairment, and personality and behavioral changes. Early detection of this disease is crucial for proper treatment. This study used MRI images to detect Alzheimer's disease, as MRI can provide a more detailed picture of brain structure and network. The method used was InceptionNet combined with data augmentation techniques to increase and increase image variation, and SMOTE (Synthetic Minority Oversampling Technique) to address class imbalance in the dataset. MRI images were processed and the model was trained with various parameter configurations, then evaluated using Loss, Accuracy, AUC, and F1-Score metrics. The best results were obtained in Model 8 with 100 epochs, a learning rate of 0.01, and a batch size of 64, with a Loss of 0.2979, Accuracy of 90.66%, AUC of 0.9851, and F1-Score of 0.9071, indicating this method is effective in supporting early detection of Alzheimer's disease. Keywords: Image Classification, Alzheimer's, MRI Images, InceptionNet, SMOTE

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Detail Information

Series Title

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Call Number

T1932752026

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xvii, 98 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.420 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

KA

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