Prediksi Parasit Plasmodium pada Citra Mikroskopis Sel Darah Merah dengan Convolutional Neural Networks
DOI:
https://doi.org/10.24002/jbi.v13i1.5007Abstract
Abstract. Prediction of Plasmodium Parasites on Microscopic Image of Red Blood Cells with Convolutional Neural Networks. Malaria is a deadly disease that attacks human blood cells caused by the plasmodium parasite. The need for fast and accurate detection in the diagnosis of malaria is certainly necessary to reduce the mortality rate from this disease. The technique for detecting the presence of Plasmodium parasites that has been widely used in routine examinations is using a microscope. With the presence of experienced medical experts, it is easy to detect the presence of Plasmodium in the blood. However, the weakness of this technique is that it relies heavily on the presence and competence of medical experts because the accuracy of microscopic examination results can decrease from 64% to 95%. The purpose of this study was to build a predictive model to classify Plasmodium parasites on red blood cell images with a good degree of accuracy with the Convolutional Neural Network algorithm. The test results show good accuracy results, namely the model of the CNN Algorithm gives an accuracy result of 97.96% and a loss of 0.06 with an average computation time of about 121 seconds/epoch.
Keywords: malaria, optimization, Plasmodium, neural network, CNN.
Abstrak.Malaria merupakan salah satu penyakit mematikan yang menyerang sel darah manusia yang disebabkan parasit plasmodium. Kebutuhan deteksi cepat dan akurat dalam diagnosis malaria tentunya sangat diperlukan untuk menekan angka kematian dari penyakit ini. Teknik deteksi keberadaan parasit Plasmodium yang telah banyak digunakan dalam pemeriksaan rutin adalah dengan menggunakan mikroskop. Dengan adanya tenaga
ahli medis yang berpengalaman, pendeteksian keberadaan Plasmodium dalam darah pun dengan mudah dilakukan. Namun kelemahan dari teknik ini adalah sangat bergantung pada keberadaan dan kompetensi dari tenaga ahli medis karena akurasi dari hasil pemeriksaan mikroskop dapat menurun 64% sampai dengan 95%. Tujuan penelitian ini adalah membangun sebuah model prediksi untuk mengklasifikasikan parasit plasmodium
pada citra sel darah merah dengan tingkat akurasi yang baik dengan algoritma Convolutional Neural Network. Hasil pengujian memperlihatkan hasil akurasi yang baik yaitu model dari Algoritma CNN ini memberikan hasil akurasi yaitu 97,96% dan loss 0,06 dengan rata-rata waktu komputasi sekitar 121 detik/epoch.
Kata kunci: malaria, deep learning, Plasmodium, neural network, CNN.
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