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Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)
https://doi.org/10.22146/ijeis.15254 Mizan Nur Khasanah(1), Agus Harjoko(2*), Ika Candradewi(3)
(1)
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM
(*) Corresponding Author
Abstract
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%.
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[1] Hiremath, P.S., Bannigidad, P., Geeta, S. 2010. Automated Identification and Classification of White Blood Cells (Leukocytes) in Digital Microscopic Images. IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition” RTIPPR, 2010 Halaman 59. Dept. of Computer Science, Gulbarga University, Gulbarga, Karnataka, India.
[2] Wiyanti, A, 2013, Multilayer Perceptron Network Clasification Of White Blood Cell's Components With Multilayer Perceptron Network, Jurnal Digilib ITS, Surabaya.
[3] Ramirez-Cortes, J.M., Gomez-Gil, P., Alarcon- Aquino, V., Gonzalez-Bernal, J., Garcia-Pedrero, A., 2011, Neural Networks and SVM-Based Classification of Leukocytes Using the Morphological Pattern Spectrum, Department of Electronics, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1 Tonantzintla, Puebla, 72840, Mexico.
[4] Widiarsana, I.G.A., 2011, Metode Klasifikasi K-Nearest Neighbor (KNN), Fakultas Teknik, Universitas Udayana, Denpasar.
[5] Simon, E, 2014, Penerapan Algoritma Jaringan Syaraf Tiruan Propagasi Balik dan ransformasi Hough untuk Deteksi Lokasi Mata Pada Citra Digital, Program Studi Teknik Informatika, STIMIK GI MDP, Palembang.
[6] Linda, A, 2012, Penerapan Region of Interest (ROI) pada Metode Kompresi JPEG200, Departemen Teknik Informarika, Institut Teknologi Bandung, Bandung.
[7] Ahmad, U, 2005, Pengolahan Citra Digital, Yogyakarta: Graha Ilmu
[8] Putra, D, 2010, Pengolahan Citra Digital, Yogyakarta: Penerbit Andi
[9] Katz, A.R.J. 2000, Image Analysis and Supervised Learning in the Automated Differentiation of White Blood Cells from Microscopic Images, Department of Computer Science, RMIT
[10] Krisandi, N, 2013, Algoritma K-Nearest Neighbor dalam Klasifikasi Data Hasil Produksi Kelapa Sawit Pada PT. Minamas Kecamatan Parindu, Buletin Ilmiah Math. Stat. dan Terapannya (Bimaster), No.1, Volume 02, halaman 33-38.
DOI: https://doi.org/10.22146/ijeis.15254
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