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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Jefferson, Jefferson
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Computer Science & IT

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IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK DALAM KLASIFIKASI SEL DARAH MERAH YANG TERPENGARUH MALARIA Harahap, Mawaddah; Jefferson, Jefferson; Barti, Surya; Samosir, Suprianto; Turnip, Christi Andika
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10713

Abstract

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers
Co-Authors Barti, Surya Mawaddah Harahap, Mawaddah Samosir, Suprianto Turnip, Christi Andika