PENERAPAN GRAY LEVEL CO-OCCURANCE MATRIX UNTUK EKSTRAKSI FITUR PADA KLASIFIKASI KANKER PAYUDARA MENGGUNAKAN CITRA USG
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Pratama, Andika Agung
Agtriadi, Herman Bedi
Kuswardani, Dwina
Agtriadi, Herman Bedi
Kuswardani, Dwina
Subject
Teknik Informatika
Datestamp
2022-09-27 04:12:41
Abstract :
Breast cancer is a disease that occurs due to growth or development of breast cells (tissue), this can occur in women and men. Breast cancer is one of the most types of cancer in Indonesia. This disease can also be suffered by men with a frequency of about 1%. In Indonesia, more than 80% of cases are found to be at an advanced stage, where treatment efforts are difficult. But in this modern era there are still some things that need to be improved in a number of medical applications, the image of the MRI, CT Scan and USG images, there is still noise which is the main cause of image quality degradation. Therefore a classification system was made using the K-Nearest Neighbor method to help classify breast cancer based on ultrasound images. K-Nearest Neighbor works by finding the number of k patterns closest to the input pattern, then determining the decision class based on high proximity values. In this research, the identification of images performs the feature extraction process, where the feature extraction is carried out by the Gray Level Co-Occurance method from various angles, namely 00, 450, 900, 1350 as the texture feature extraction.
Breast cancer is a disease that occurs due to growth or development of breast cells (tissue), this can occur in women and men. Breast cancer is one of the most types of cancer in Indonesia. This disease can also be suffered by men with a frequency of about 1%. In Indonesia, more than 80% of cases are found to be at an advanced stage, where treatment efforts are difficult. But in this modern era there are still some things that need to be improved in a number of medical applications, the image of the MRI, CT Scan and USG images, there is still noise which is the main cause of image quality degradation. Therefore a classification system was made using the K-Nearest Neighbor method to help classify breast cancer based on ultrasound images. K-Nearest Neighbor works by finding the number of k patterns closest to the input pattern, then determining the decision class based on high proximity values. In this research, the identification of images performs the feature extraction process, where the feature extraction is carried out by the Gray Level Co-Occurance method from various angles, namely 00, 450, 900, 1350 as the texture feature extraction.
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