KLASIFIKASI PENYAKIT PADA TULANG PUNGGUNG MENGGUNAKAN METODE J48 DAN BAGGING

• Main Authors: , MONICA AGUSTAMI KRISTY, , Indriana Hidayah, S.T., M.T.
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2013
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/124254/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=64374
• Institution: Universitas Gadjah Mada

Vertebral column as a part of backbone has important role in the human body. Trauma in vertebral column can affect spinal cord capability to send and receive messages from brain to the body systems that control sensory and motor. Disc hernia and spondylolisthesis are examples of phatology on the vertebral column. Research about phatology or damage bones and joints of skeletal system classification is rarely. Whereas the classification system can be used by radiologists as a �second opinion� so that can improve productivity and diagnosis consistency from that radiologists. This research use dataset Vertebral Colum that has three classes (Hernia, Spondylolisthesis, Normal) and 310 instances in UCI Machine Learning. This research used two methods, that are decision tree (J48) and Bagging. Decision tree is a method that easy to be represented or understood. But, decision tree is an unstable method. Decision tree not necessarily give the same prediction when be given new test instance, especially if training data that used is small. Whereas Bagging was one of ensemble method that can overcome that unstability. Purpose of this research is to determine the method from two method that used in this research so that can be used for classification of phatology on the vertebral column. 10-fold cross-validation used as evaluation method. Whereas TP rate, FP rate, accuracy, and ROC AUC used as parameter evaluation. Dataset will classified and evaluated using software WEKA 3.6.9. The results showed that Bagging has better performance than decision tree (J48). The accuration of Bagging is 85.1613%. FP rate of hernia class is 0.683, spondylolisthesis class is 0.967, normal class is 0.78. TP rate of hernia class is 0.076, spondylolisthesis class is 0.025, normal class is 0.11. ROC AUC of hernia class is 0.942, spondylolisthesis class is 0.988, normal class is 0.927.