Klasifikasi Malaria melalui Penggunaan Convolutional Neural Network pada Citra Sel Darah
DOI:
https://doi.org/10.55606/srjyappi.v1i6.853Keywords:
Malaria, Convolutional Neural Networks (CNN), Data AugmentationAbstract
Malaria is a global health challenge with significant impact in various countries. This study aims to analyze the effect of data augmentation on the performance of Convolutional Neural Networks (CNN) in malaria classification. The dataset used involves red blood cell images, with the main focus on the detection of Plasmodium parasites as the cause of malaria. Two CNN models were implemented, namely the model without data augmentation and the model with data augmentation. The model without augmentation showed a test score of 94%, while the model with augmentation showed a significant performance improvement with a test score of 96%. The overall accuracy of the augmented model on the test set reached 94%, signifying a significant improvement in diagnostic accuracy.
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Copyright (c) 2023 Rabiahtul Adawiah Hasyani, Silvana Maretha Simbolon, Yasmin Mufida, Yolanda Ester Berliana Ritonga
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