TY - GEN
T1 - Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels
AU - Kindhi, Berlian Al
AU - Susanto, Noviyanti
AU - Handayani, Wuri
AU - Kurniasari, Septiana Vera
AU - Pratama, Afriliya Putri
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/9
Y1 - 2021/4/9
N2 - Tuberculosis is a contagious disease that is generally transmitted through a sufferer's cough and is deadly. Tuberculosis usually attacks the lungs but can also affect other parts of the body. Treatment of tuberculosis patients who do not recover with those who can recover is different, mishandling can cause death in patients therefore, we need a system that can predict whether the patient's condition can recover normally, or the lungs cannot be recovered. Support vector machine is a learning system that uses a hypothetical linear function in a high dimensional space and is trained with an algorithm based on optimization theory by applying learning bias derived from statistical theory. In this study, the kernel function is used, namely the radial kernel and the polynomial. Based on the analysis and discussion that has been done, it can be concluded from this study that the performance of the radial and polynomial kernels is the same with an accuracy of 85% and a sensitivity value of 94%.
AB - Tuberculosis is a contagious disease that is generally transmitted through a sufferer's cough and is deadly. Tuberculosis usually attacks the lungs but can also affect other parts of the body. Treatment of tuberculosis patients who do not recover with those who can recover is different, mishandling can cause death in patients therefore, we need a system that can predict whether the patient's condition can recover normally, or the lungs cannot be recovered. Support vector machine is a learning system that uses a hypothetical linear function in a high dimensional space and is trained with an algorithm based on optimization theory by applying learning bias derived from statistical theory. In this study, the kernel function is used, namely the radial kernel and the polynomial. Based on the analysis and discussion that has been done, it can be concluded from this study that the performance of the radial and polynomial kernels is the same with an accuracy of 85% and a sensitivity value of 94%.
KW - Polynomial Kernel
KW - Radial Basis Function
KW - SVM
KW - Tuberculosis
UR - http://www.scopus.com/inward/record.url?scp=85107288401&partnerID=8YFLogxK
U2 - 10.1109/EIConCIT50028.2021.9431878
DO - 10.1109/EIConCIT50028.2021.9431878
M3 - Conference contribution
AN - SCOPUS:85107288401
T3 - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
SP - 365
EP - 368
BT - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
A2 - Alfred, Rayner
A2 - Haviluddin, Haviluddin
A2 - Wibawa, Aji Prasetya
A2 - Santoso, Joan
A2 - Kurniawan, Fachrul
A2 - Junaedi, Hartarto
A2 - Purnawansyah, Purnawansyah
A2 - Setyati, Endang
A2 - Saurik, Herman Thuan To
A2 - Setiawan, Esther Irawati
A2 - Setyaningsih, Eka Rahayu
A2 - Pramana, Edwin
A2 - Kristian, Yosi
A2 - Kelvin, Kelvin
A2 - Purwanto, Devi Dwi
A2 - Kardinata, Eunike
A2 - Anugrah, Prananda
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Y2 - 9 April 2021 through 11 April 2021
ER -
