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

Abstract

<p><strong>Abstract - 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.</strong></p><p><strong> </strong><strong>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%.</strong></p>

Mikroskop Digital, Otomatis, dan Portabel berbasis Raspberry Pi dengan Catu Daya DC

“…Citra digital sampel hapusan darah yang sudah dapat ditampilkan di layar LCD dengan baik selanjutnya dapat diolah menggunakan operasi pengolahan citra [21][22][23] ataupun kecerdasan buatan seperti Convolutional Neural Network (CNN) [24][25][26][27] pada platform raspberry pi menggunakan bahasa pemrograman python sehingga hasil pendeteksian parasit ataupun penghitungan jumlah parasit pada sampel dapat ditampilkan pada layar LCD. Untuk memudahkan pengguna dalam mengoperasikan perangkat lunak pada komputer mini ini maka diperlukan juga perancangan Graphical User Interface yang juga dapat ditampilkan pada layar LCD.…”

Detection Malaria Base Microscope Digital Image with Convolutional Neural Network

“…In this study, using the Modified K-Means (MK-Means) and Backpropagation methods, the dataset was obtained from the Public Health Image Library (PHIL) website. This study received malaria detection results with an accuracy rate of 86.66% (Hamid et al, 2016).…”

“…(Banyal & Dayat, 2016) aimed to detect malaria plasmodium through blood cell images, results of this study used the SVM method with an accuracy of 75%. Research conducted (Hamid et al, 2016) aimed to create a tool to identify the presence of plasmodium parasite cells in blood cell images, results used Backpropagation method with an accuracy rate of 86.8%.…”