Klasifikasi Malaria melalui Penggunaan Convolutional Neural Network pada Citra Sel Darah

Authors

  • Rabiahtul Adawiah Hasyani Universitas Negeri Medan
  • Silvana Maretha Simbolon Universitas Negeri Medan
  • Yasmin Mufida Universitas Negeri Medan
  • Yolanda Ester Berliana Ritonga Universitas Negeri Medan

DOI:

https://doi.org/10.55606/srjyappi.v1i6.853

Keywords:

Malaria, Convolutional Neural Networks (CNN), Data Augmentation

Abstract

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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Published

2023-12-09

How to Cite

Rabiahtul Adawiah Hasyani, Silvana Maretha Simbolon, Yasmin Mufida, & Yolanda Ester Berliana Ritonga. (2023). Klasifikasi Malaria melalui Penggunaan Convolutional Neural Network pada Citra Sel Darah. Student Research Journal, 1(6), 287–298. https://doi.org/10.55606/srjyappi.v1i6.853

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