Skripsi

KLASIFIKASI PENYAKIT STROKE DENGAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN METODE EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI)

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Penilaian

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Stroke is a disease in which the sufferer experiences bleeding or blockage of the brain arteries. The focus of this research is the use of Convolutional Neural Network (CNN) method in stroke disease classification and Explainable Artificial Intelligence (XAI) method as an approach to understand more deeply the decision-making process used by the CNN model. There are 3 models used by OzNet, ResNet50V2 and EfficientNetV2, This classification uses the dataset "Brain Stroke CT Image Dataset" obtained from Kaggle. This dataset contains 2501 images of brain CT scans categorized as "Normal" and "Stroke". The classifier obtained a testing accuracy of 97% for the OzNet model, 98% for the ResNet50V2 model and 97% for the EfficientNetV2 model. For the XAI approach, the heatmap showed more specific results for the ResNet50V2 model.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002927	T144506	T1445062024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1445062024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2024

Collation

xv, VI-1 hlm.; ilus.; tab.; 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

618.810 7

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Prodi Teknik Informatika
Convolutional Neural Network (CNN)

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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