Klasifikasi Luka pada Jaringan Payudara Berbasis Spektra Impedansi Listrik Menggunakan Fuzzy k-Nearest Neighbor - Sandya Ratna Maruti; Imam Cholissodin; Heru Nurwarsito (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Breast disease generally occurs in women with increased incidence of disease each year. The mortality rate for the sufferer is up to 40% and above and tends to be in modern young women. Therefore breast cancer detection and early diagnosis of the stage become the most important problem in medicine. The physiopathology state of human breast tissue can be seen with Electrical Impedance Spectral (EIS) so that it can be classified. The aim of this research is to classify the wound on breast tissue and to know the accuracy using Fuzzy k-Nearest Neighbor (FKNN) method. The dataset consists of 105 data, from the UCI-Repository dataset with 9 input parameters obtained from electrical impedance including I0, PA500, HFS, DA, AREA, A / DA, MAX IP, DR and P. While the output is a condition of breast injury that is glandular tissue, connective tissue, adipose tissue, mastopathy, fibro-adenoma and carcinoma. The FKNN test yields the best value of m = 2, the percentage of training data = 60% and k = 3. The result of this method is able to classify 28 data testing in accordance with the actual class and 14 data testing which is not in accordance with the actual class of total 42 data testing. The accuracy rate is 66.6666667%.