| dc.description.abstract | Malaria is a disease that is transmitted through the bite of a female Anopheles mosquito that contains the parasite genus Plasmodium. This disease infects human red blood cells. Plasmodium vivax is a type of parasite that causes malaria, which is known as the type of malaria with the widest distribution area, from tropical, subtropical to cold climates. The diagnosis of malaria infection is still manual, using a microscope as the main test for diagnosing malaria infection. The results of the diagnosis from paramedics based on the intensity, morphology, texture of red blood cells. This allows for errors, such as the presence of small parts of red blood cells that are missed by the human eye, the condition of the available equipment, the experience of paramedics, blood staining techniques, and requires a long examination time. To overcome this problem, a method is needed to automatically identify malaria parasites on red blood cells In this research, to identify the malaria parasite Plasmodium vivax, the Probabilistic Neural Network method was used.The steps taken before identification are preprocessing using Green Channel, Contrast Limited Adaptive Histogram Equalization (CLAHE), Morphological Close and Background Exclusion, then segmentation with Otsu Thresholding, next step is post-processing with Connected Component Analyst (CCA) and feature extraction with Invariant Moment. The results of this research showed that the method used was able to identify the malaria parasite plasmodium vivax on microscopic images of reb blood cells with an accuracy rate of 97.14%. | en_US |
