KOMBINASI ALGORITMA CART DAN RIPPER UNTUK MENDIAGNOSIS PENYAKIT LIVER BERBASIS CORRELATION BASED FEATURE SELECTION

Restiani, Dian (2018) KOMBINASI ALGORITMA CART DAN RIPPER UNTUK MENDIAGNOSIS PENYAKIT LIVER BERBASIS CORRELATION BASED FEATURE SELECTION. Other thesis, STMIK Amikom Purwokerto.

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Abstract

The liver is the largest organ and important for the body. The human can not live without the heart. Liver disease is not easy to find in the early stages. For detection of liver disease patients in the early stages will prolong the patient's age. To detect liver disease, the patient must perform a blood test. Mistakes in diagnosing illness and determining drug consumption can cause harm to the health of the patient and even cause death. This allows researchers and practitioners to focus on detecting/diagnosing liver disease and preventing it because it can lead to death if it is acute. The method used in this research is CRISP-DM. The algorithm used in this study is a combination of CART and RIPPER using dataset taken from the UCI Indian Liver patient data repository Dataset consisting of clinical data of patients who detected positive and negative liver disease. The result of this research by using combination of rule result from CART algorithm and RIPPER algorithm obtained accuracy 66%, on the ILPD dataset without performing feature selection, whereas on the accuracy results of the ILPD dataset by performing feature selection is accuracy 69%. From these result it can be concluded that research using feature selection a highher accuracy.

Item Type: Thesis (Other)
Additional Information: Dosen Pembimbing: Mohammad Imron, M.Kom.
Uncontrolled Keywords: Diagnosis, Liver Disease, Algorithm, CRISP-DM, CART, RIPPER, Feature Selection
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: UPT Perpustakaan Pusat Universitas Amikom Purwokerto
Date Deposited: 14 Oct 2021 02:57
Last Modified: 14 Oct 2021 02:57
URI: http://eprints.amikompurwokerto.ac.id/id/eprint/1064

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