KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF)
Colon is an important organ in the human body. Many disorders that can affect these organs, and the worst is cancer. There are several types of cancer including colorectal is carcinoma and limphoma, two types of cancer is a malignant cancer. So if the cancer not quickly detected and classified, it w...
| Príomhchruthaitheoirí: | , |
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| Formáid: | Tráchtas |
| Foilsithe / Cruthaithe: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| _version_ | 1826048974231961600 |
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| author | , NIKITE SULISTIYANA , Drs. Agus Harjoko, M.Sc.,Ph.D |
| author_facet | , NIKITE SULISTIYANA , Drs. Agus Harjoko, M.Sc.,Ph.D |
| author_sort | , NIKITE SULISTIYANA |
| collection | UGM |
| description | Colon is an important organ in the human body. Many disorders that can affect these organs, and the worst is cancer. There are several types of cancer including colorectal is carcinoma and limphoma, two types of cancer is a malignant cancer. So if the cancer not quickly detected and classified, it will cause death. Currently colon cancer classification identified by manual. So diagnosing highly correlated with the quality of vision of each doctor. Human error will greatly affect the outcome of diagnosing. Therefore in this research creates a system to classify colon cancer based on image processing and the data take from a digital image of tissue colorectal cancer.
To classify colorectal cancer used Artificial Neural Network Radial Basis Function. Used 300 sample images to extracted the features using gray level cooccurence matrix (GLCM). The features is the energy, contrast, correlation, and homogeneity. This features are trained using the Radial Basis Function neural network in order to classify the image into 3 class is carcinoma, lymphoma, and normal. From the testing is obtained accuracy rate is 91.11% of 90 test images with the RBF neural network parameters: the epoch 5000, learning rate is 0.01 and hidden layer neurons or the centers is 175. |
| first_indexed | 2024-03-13T23:38:31Z |
| format | Thesis |
| id | oai:generic.eprints.org:133188 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T23:38:31Z |
| publishDate | 2014 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1331882016-03-04T08:03:25Z https://repository.ugm.ac.id/133188/ KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) , NIKITE SULISTIYANA , Drs. Agus Harjoko, M.Sc.,Ph.D ETD Colon is an important organ in the human body. Many disorders that can affect these organs, and the worst is cancer. There are several types of cancer including colorectal is carcinoma and limphoma, two types of cancer is a malignant cancer. So if the cancer not quickly detected and classified, it will cause death. Currently colon cancer classification identified by manual. So diagnosing highly correlated with the quality of vision of each doctor. Human error will greatly affect the outcome of diagnosing. Therefore in this research creates a system to classify colon cancer based on image processing and the data take from a digital image of tissue colorectal cancer. To classify colorectal cancer used Artificial Neural Network Radial Basis Function. Used 300 sample images to extracted the features using gray level cooccurence matrix (GLCM). The features is the energy, contrast, correlation, and homogeneity. This features are trained using the Radial Basis Function neural network in order to classify the image into 3 class is carcinoma, lymphoma, and normal. From the testing is obtained accuracy rate is 91.11% of 90 test images with the RBF neural network parameters: the epoch 5000, learning rate is 0.01 and hidden layer neurons or the centers is 175. [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , NIKITE SULISTIYANA and , Drs. Agus Harjoko, M.Sc.,Ph.D (2014) KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF). UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=73748 |
| spellingShingle | ETD , NIKITE SULISTIYANA , Drs. Agus Harjoko, M.Sc.,Ph.D KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) |
| title | KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) |
| title_full | KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) |
| title_fullStr | KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) |
| title_full_unstemmed | KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) |
| title_short | KLASIFIKASI KANKER USUS BESAR BERBASIS PENGOLAHAN CITRA DIGITAL DENGAN METODE RADIAL BASIS FUNCTION (RBF) |
| title_sort | klasifikasi kanker usus besar berbasis pengolahan citra digital dengan metode radial basis function rbf |
| topic | ETD |
| work_keys_str_mv | AT nikitesulistiyana klasifikasikankerususbesarberbasispengolahancitradigitaldenganmetoderadialbasisfunctionrbf AT drsagusharjokomscphd klasifikasikankerususbesarberbasispengolahancitradigitaldenganmetoderadialbasisfunctionrbf |