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

WIJAYA, SYAHRANI PUSPITA and Kurniati, Rizki and Rachmatullah, Muhammad Naufal (2024) KLASIFIKASI PENYAKIT STROKE DENGAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN METODE EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI). Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Convolutional Neural Network, Explainable Artificial Intelligence, Klasifikasi, Stroke.
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5.A142 Computer science. Information society. Information technology. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Syahrani Puspita Wijaya
Date Deposited:	21 May 2024 02:10
Last Modified:	21 May 2024 02:10
URI:	http://repository.unsri.ac.id/id/eprint/144506

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