PERBANDINGAN METODE DECISION TREE DENGAN NAÏVE BAYES DALAM KLASIFIKASI TUMOR OTAK CITRA MRI

• Main Author: Suci Dilasari Kamil, (Author)
• Format: Book
• Published: 2020-06-20.
• Subjects: Thesis; NonPeerReviewed
• Online Access: Link Metadata

Summary

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 Description

http://repository.upnvj.ac.id/6844/13/ABSTRAK.pdf
http://repository.upnvj.ac.id/6844/14/AWAL.pdf
http://repository.upnvj.ac.id/6844/15/BAB%201.pdf
http://repository.upnvj.ac.id/6844/16/BAB%202.pdf
http://repository.upnvj.ac.id/6844/17/BAB%203.pdf
http://repository.upnvj.ac.id/6844/18/BAB%204.pdf
http://repository.upnvj.ac.id/6844/19/BAB%205.pdf
http://repository.upnvj.ac.id/6844/20/DAFTAR%20PUSTAKA.pdf
http://repository.upnvj.ac.id/6844/21/RIWAYAT%20HIDUP.pdf
http://repository.upnvj.ac.id/6844/22/LAMPIRAN.pdf
http://repository.upnvj.ac.id/6844/23/ARTIKEL%20KI.pdf