KLASIFIKASI MALARIA PADA CITRA APUSAN DARAH MENGGUNAKAN ARSITEKTUR INCEPTION V3 DAN XCEPTION
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Abstract
Plasmodium is the species that causes malaria and is carried by the bite of a female mosquito. According to World Health Organization records, malaria cases continue to increase from year to year and have become a major world problem. Accordingly, a system is needed to diagnose malaria parasites accurately and quickly, one of which is by utilizing Deep Learning. This research builds a system that is able to detect the presence of malaria parasites using blood smear images with two Convolutional Neural Network (CNN) architectures, namely InceptionV3 and Xception. After the model is trained and tested, an evaluation is carried out to measure the model's performance. An accuracy value of 97% was obtained for the InceptionV3 architecture and 91% for the Exception architecture. So it can be concluded that the InceptionV3 CNN architecture is the best method for diagnosing malaria parasites.
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