Penerapan Fitur Warna Untuk Identifikasi Plasmodium Falciparum pada Sediaan Apus Darah Menggunakan MK-Means dan Jaringan Backpropagation

This research proposed a system to identify Plasmodium falciparum on blood smear using the neural network backpropagation. Modified K-Means (MK-Means) is used to separate between the object with the background image because that method was able to equalize the value of fitness at all Center cluster so there is no dead center and can also cope with the local minimum value. The extraction of the features used in this study consists of color features i.e. calculation of the mean, standard deviation, skewness, curtosis and entropy of co-occurent matrix with the purpose to get the values of all the trait value image, obtained are then used to train a neural network with the backpropagation training algorithm. Method of backpropagation networks capable of acquiring knowledge even though there is no certainty, able to perform a generalization and extraction of a specific data pattern. The image of the preparations blood smear are classified using the method of neural network Backpropagation. The test results obtained from Tropozoit with the accuracy 100%, scizon 80% and gametocytes 80%. Identification is then obtained outcomes the introduction with an average accuracy of 86,66%.