Open AccessKlasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)
Author(s) -
Mizan Nur Khasanah,
Agus Harjoko,
Ika Candradewi
Publication year - 2016
Publication title -
ijeis (indonesian journal of electronics and instrumentation system)/ijeis (indonesian journal of electronics and instrumentation systems)
Language(s) - English
Resource type - Journals
eISSN - 2460-7681
pISSN - 2088-3714
DOI - 10.22146/ijeis.15254
Subject(s) - thresholding , pattern recognition (psychology) , artificial intelligence , segmentation , k nearest neighbors algorithm , white blood cell , computer science , feature extraction , feature (linguistics) , image (mathematics) , medicine , linguistics , philosophy
The traditional procedure of classification of blood cells using a microscope in the laboratory of hematology to obtain information types of blood cells. It has become a cornerstone in the laboratory of hematology to diagnose and monitor hematologic disorders. However, the manual procedure through a series of labory test can take a while. Thresfore, this research can be helpful in the early stages of the classification of white blood cells automatically in the medical field.Efforts to overcome the length of time and for the purposes of early diagnose can use the image processing technique based on morphology of blood cells. This research aims to classify the white blood cells based on cell morphology with the k-nearest neighbor (knn). Image processing algorithms used hough circle, thresholding, feature extraction, then to the process of classification was used the method of k-nearest neighbor (knn).In the process of testing used 100 images to be aware of its kind. The test results showed segmentation accuracy of 78% and testing the classification of 64%.