Implementation Of C5.0 Classification And Support Vector Machine
Algorithm With Correlation-Based Feature Selection In Application
Liver Disease
Dublin Core
Title
Implementation Of C5.0 Classification And Support Vector Machine
Algorithm With Correlation-Based Feature Selection In Application
Liver Disease
Algorithm With Correlation-Based Feature Selection In Application
Liver Disease
Subject
Liver; C5.0; Support Vector Machine; Radial Basis Function (RBF); Sigmoid; CFS (Correlation Based Future Selection)
Description
Liver disease is a general term that refers to a number of disorders or problems that affect the liver. The liver is an important
organ in the human body and has many diverse functions, including food processing, protein production, toxin removal, and energy
storage. Therefore, when the liver experiences disorders or disease, it can have a serious impact on the overall health and function of
the body. Liver disease is a significant global health problem. Early detection as well as classification of liver disease can provide
valuable guidance for effective treatment. Based on the problems above, the aim of this research is to create a liver disease classification
model using C5.0 and Support Vector Machine with Radial Basis Function (RBF) and Sigmoid kernels. With data obtained from the
liver disease dataset. The two methods will be compared and we will find out which one produces the best results. The method used is
also optimized with CFS (Correlation Based Feature Selection) feature selection. The results of the classification process, namely the
C5.0 Model and Support Vector Machine (RBF) with CFS have a similar accuracy of 76%, while the Support Vector Machine (Sigmoid)
has an accuracy of 70%, without feature selection the C5.0 algorithm has an accuracy of 66% , Support Vector Machine between RBF
and sigmoid kernels has an accuracy of 69% and 55%.
organ in the human body and has many diverse functions, including food processing, protein production, toxin removal, and energy
storage. Therefore, when the liver experiences disorders or disease, it can have a serious impact on the overall health and function of
the body. Liver disease is a significant global health problem. Early detection as well as classification of liver disease can provide
valuable guidance for effective treatment. Based on the problems above, the aim of this research is to create a liver disease classification
model using C5.0 and Support Vector Machine with Radial Basis Function (RBF) and Sigmoid kernels. With data obtained from the
liver disease dataset. The two methods will be compared and we will find out which one produces the best results. The method used is
also optimized with CFS (Correlation Based Feature Selection) feature selection. The results of the classification process, namely the
C5.0 Model and Support Vector Machine (RBF) with CFS have a similar accuracy of 76%, while the Support Vector Machine (Sigmoid)
has an accuracy of 70%, without feature selection the C5.0 algorithm has an accuracy of 66% , Support Vector Machine between RBF
and sigmoid kernels has an accuracy of 69% and 55%.
Creator
Andi Nur Rachman a
, Cecep Muhamad Sidiq a,*
, Muhammad Hanif Insanib
, Cecep Muhamad Sidiq a,*
, Muhammad Hanif Insanib
Source
https://jurnal.unsil.ac.id/index.php/jaisi/article/view/10848/3413
Publisher
a Department of Information System, Siliwangi Universty, Tasikmalaya, Indonesia
bDepartment of Informatics, Siliwangi Universty, Tasikmalaya, Indonesia
bDepartment of Informatics, Siliwangi Universty, Tasikmalaya, Indonesia
Date
Mei 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Andi Nur Rachman a
, Cecep Muhamad Sidiq a,*
, Muhammad Hanif Insanib, “Implementation Of C5.0 Classification And Support Vector Machine
Algorithm With Correlation-Based Feature Selection In Application
Liver Disease,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8364.
Algorithm With Correlation-Based Feature Selection In Application
Liver Disease,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8364.