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Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification

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@article{IJASEIT9243,
   author = {Zuherman Rustam and Jacub Pandelaki and Dea Aulia Utami and Rahmat Hidayat and Azizul Azhar Ramli},
   title = {Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {6},
   year = {2019},
   pages = {2142--2146},
   keywords = {fuzzy C-Means clustering; fuzzy possibilistic C-Means; FPCMK; osteoarthritis knee; support vector machine.},
   abstract = {Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9243},
   doi = {10.18517/ijaseit.9.6.9243}
}

EndNote

%A Rustam, Zuherman
%A Pandelaki, Jacub
%A Utami, Dea Aulia
%A Hidayat, Rahmat
%A Ramli, Azizul Azhar
%D 2019
%T Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification
%B 2019
%9 fuzzy C-Means clustering; fuzzy possibilistic C-Means; FPCMK; osteoarthritis knee; support vector machine.
%! Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification
%K fuzzy C-Means clustering; fuzzy possibilistic C-Means; FPCMK; osteoarthritis knee; support vector machine.
%X Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9243
%R doi:10.18517/ijaseit.9.6.9243
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 6
%@ 2088-5334

IEEE

Zuherman Rustam,Jacub Pandelaki,Dea Aulia Utami,Rahmat Hidayat and Azizul Azhar Ramli,"Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, pp. 2142-2146, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.6.9243.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Rustam, Zuherman
AU  - Pandelaki, Jacub
AU  - Utami, Dea Aulia
AU  - Hidayat, Rahmat
AU  - Ramli, Azizul Azhar
PY  - 2019
TI  - Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 6
Y2  - 2019
SP  - 2142
EP  - 2146
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - fuzzy C-Means clustering; fuzzy possibilistic C-Means; FPCMK; osteoarthritis knee; support vector machine.
N2  - Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9243
DO  - 10.18517/ijaseit.9.6.9243

RefWorks

RT Journal Article
ID 9243
A1 Rustam, Zuherman
A1 Pandelaki, Jacub
A1 Utami, Dea Aulia
A1 Hidayat, Rahmat
A1 Ramli, Azizul Azhar
T1 Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 6
YR 2019
SP 2142
OP 2146
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 fuzzy C-Means clustering; fuzzy possibilistic C-Means; FPCMK; osteoarthritis knee; support vector machine.
AB Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9243
DO  - 10.18517/ijaseit.9.6.9243