Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels

Berlian Al Kindhi; Noviyanti Susanto; Wuri Handayani; Septiana Vera Kurniasari; Afriliya Putri Pratama

doi:10.1109/EIConCIT50028.2021.9431878

Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels

Berlian Al Kindhi
, Noviyanti Susanto
, Wuri Handayani
, Septiana Vera Kurniasari
, Afriliya Putri Pratama

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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%.

Original language	English
Title of host publication	3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Editors	Rayner Alfred, Haviluddin Haviluddin, Aji Prasetya Wibawa, Joan Santoso, Fachrul Kurniawan, Hartarto Junaedi, Purnawansyah Purnawansyah, Endang Setyati, Herman Thuan To Saurik, Esther Irawati Setiawan, Eka Rahayu Setyaningsih, Edwin Pramana, Yosi Kristian, Kelvin Kelvin, Devi Dwi Purwanto, Eunike Kardinata, Prananda Anugrah
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	365-368
Number of pages	4
ISBN (Electronic)	9781665405140
DOIs	https://doi.org/10.1109/EIConCIT50028.2021.9431878
Publication status	Published - 9 Apr 2021
Event	3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 - Virtual, Surabaya, Indonesia Duration: 9 Apr 2021 → 11 Apr 2021

Publication series

Name	3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

Conference

Conference	3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Country/Territory	Indonesia
City	Virtual, Surabaya
Period	9/04/21 → 11/04/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Polynomial Kernel
Radial Basis Function
SVM
Tuberculosis

Access to Document

10.1109/EIConCIT50028.2021.9431878

Cite this

Kindhi, B. A., Susanto, N., Handayani, W., Kurniasari, S. V., & Pratama, A. P. (2021). Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels. In R. Alfred, H. Haviluddin, A. P. Wibawa, J. Santoso, F. Kurniawan, H. Junaedi, P. Purnawansyah, E. Setyati, H. T. T. Saurik, E. I. Setiawan, E. R. Setyaningsih, E. Pramana, Y. Kristian, K. Kelvin, D. D. Purwanto, E. Kardinata, & P. Anugrah (Eds.), 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 (pp. 365-368). Article 9431878 (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EIConCIT50028.2021.9431878

Kindhi, Berlian Al ; Susanto, Noviyanti ; Handayani, Wuri et al. / Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021. editor / Rayner Alfred ; Haviluddin Haviluddin ; Aji Prasetya Wibawa ; Joan Santoso ; Fachrul Kurniawan ; Hartarto Junaedi ; Purnawansyah Purnawansyah ; Endang Setyati ; Herman Thuan To Saurik ; Esther Irawati Setiawan ; Eka Rahayu Setyaningsih ; Edwin Pramana ; Yosi Kristian ; Kelvin Kelvin ; Devi Dwi Purwanto ; Eunike Kardinata ; Prananda Anugrah. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 365-368 (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021).

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title = "Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels",

abstract = "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\%.",

keywords = "Polynomial Kernel, Radial Basis Function, SVM, Tuberculosis",

author = "Kindhi, \{Berlian Al\} and Noviyanti Susanto and Wuri Handayani and Kurniasari, \{Septiana Vera\} and Pratama, \{Afriliya Putri\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 ; Conference date: 09-04-2021 Through 11-04-2021",

year = "2021",

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day = "9",

doi = "10.1109/EIConCIT50028.2021.9431878",

language = "English",

series = "3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "365--368",

editor = "Rayner Alfred and Haviluddin Haviluddin and Wibawa, \{Aji Prasetya\} and Joan Santoso and Fachrul Kurniawan and Hartarto Junaedi and Purnawansyah Purnawansyah and Endang Setyati and Saurik, \{Herman Thuan To\} and Setiawan, \{Esther Irawati\} and Setyaningsih, \{Eka Rahayu\} and Edwin Pramana and Yosi Kristian and Kelvin Kelvin and Purwanto, \{Devi Dwi\} and Eunike Kardinata and Prananda Anugrah",

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Kindhi, BA, Susanto, N, Handayani, W, Kurniasari, SV & Pratama, AP 2021, Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels. in R Alfred, H Haviluddin, AP Wibawa, J Santoso, F Kurniawan, H Junaedi, P Purnawansyah, E Setyati, HTT Saurik, EI Setiawan, ER Setyaningsih, E Pramana, Y Kristian, K Kelvin, DD Purwanto, E Kardinata & P Anugrah (eds), 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021., 9431878, 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 365-368, 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, Virtual, Surabaya, Indonesia, 9/04/21. https://doi.org/10.1109/EIConCIT50028.2021.9431878

Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels. / Kindhi, Berlian Al; Susanto, Noviyanti; Handayani, Wuri et al.
3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021. ed. / Rayner Alfred; Haviluddin Haviluddin; Aji Prasetya Wibawa; Joan Santoso; Fachrul Kurniawan; Hartarto Junaedi; Purnawansyah Purnawansyah; Endang Setyati; Herman Thuan To Saurik; Esther Irawati Setiawan; Eka Rahayu Setyaningsih; Edwin Pramana; Yosi Kristian; Kelvin Kelvin; Devi Dwi Purwanto; Eunike Kardinata; Prananda Anugrah. Institute of Electrical and Electronics Engineers Inc., 2021. p. 365-368 9431878 (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85107288401

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 -

Kindhi BA, Susanto N, Handayani W, Kurniasari SV, Pratama AP. Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels. In Alfred R, Haviluddin H, Wibawa AP, Santoso J, Kurniawan F, Junaedi H, Purnawansyah P, Setyati E, Saurik HTT, Setiawan EI, Setyaningsih ER, Pramana E, Kristian Y, Kelvin K, Purwanto DD, Kardinata E, Anugrah P, editors, 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 365-368. 9431878. (3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021). doi: 10.1109/EIConCIT50028.2021.9431878

Prediction of the Tuberculosis Patients Who Can Recover Normally Using a Support Vector Machine with Radial and Polynomial Kernels

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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