Suci Dilasari Kamil, . (2020) PERBANDINGAN METODE DECISION TREE DENGAN NAÏVE BAYES DALAM KLASIFIKASI TUMOR OTAK CITRA MRI. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
In medical image classification, Machine Learning algorithm is commonly implemented. Decision Tree and Naive Bayes are commonly used method in medical image classification. Therefore, a comparison between Decision Tree and Naive Bayes algorithm is concluded to get the performance of the classification methods to MRI, with preprocess of grayscale, K- means clustering for segmentation, and GLCM for texture feature extraction. This study will implement texture analysis with contrast, correlation, energy, and homogeneity to classify the images to two class: brain tumor and non-brain tumor. From the study, based on the value of accuracy, specificity, and sensitivity, Decision Tree has higher values compared to Naive Bayes which are 96% accuracy, 96% specificity, and 96% sensitivity compared to Naive Bayes value of 91% accuracy, 90% specificity, and 93% sensitivity.
Item Type: | Thesis (Skripsi) |
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Additional Information: | [No. Panggil : 1610511023] [Pembimbing 1 : Didit widiyanto] [Pembimbing 2 : Nurul Chamidah] [Penguji 1 : Yuni WIdiastiwi] [Penguji 2 : Bambang Tri Wahyono] |
Uncontrolled Keywords: | Keywords: Classification, MRI Image, Comparison, Decision Tree, Naïve Bayes, |
Subjects: | T Technology > T Technology (General) |
Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
Depositing User: | Suci Dilasari Kamil |
Date Deposited: | 12 Jan 2022 04:53 |
Last Modified: | 12 Jan 2022 04:53 |
URI: | http://repository.upnvj.ac.id/id/eprint/6844 |
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