Detail Publikasi

Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Citra Sel Pap Smear Menggunakan Analisis Tekstur Nukleus

• By TONI ARIFIN

Abstrak

Cervical cancer is the one of cause women death in the world. At least every 2 minutes 1 people death it cause of cervical cancer. One of prevention to early detection of cervical cancer is Pap Smear examination. Pap Smear test conducted to determine infection or abnormal cell that can turn into cancer cell. In this research used texture analysis data obtained from the result of image processing cell nucleus of normal and abnormal Pap Smear and 7 class Pap Smear cells is Normal Superficial (NS), Normal Intermediate (NI), Normal Columnar (NC), Mild (Light) Dysplasia (MLD), Severe Dysplasia (SD), Moderate Dysplasia (MD), Carcinoma In Situ (CIS). Image data derived from the data Harlev is totaling 280 images. The method of this research is used classification K-nearest neighbor method and for testing is used Confusion Matrix to see how much accuracy is generated by using K-nearest neighbor method. The result accuracy of normal and abnormal classification is 73,10% and for class classification is 33,33%.

Kata Kunci:
Texture Analysis, K-nearest neighbor , Classification, Pap Smear Cell, Cervical Cancer, Confusion Matrix.