Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
KOMPARASI ALGORITMA MULTI LAYER PERCEPTRON DAN RADIAL BASIS FUNCTION UNTUK DIAGNOSA PENYAKIT JANTUNG
Keywords:
neural network, Multilayer Perceptron, Radial Basis Function, Diagnosa Penyakit Jantung
Abstract
A neural network as a data mining model has many algorithms with different accuracy level. This research uses the UCI machine learning repository’s data to compare the accuracy level of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithm in predicting the heart disease. Confusion matrix and the ROC (Receiver Operating Characteristic) curve method is used to measure the performance of both algorithms. Based on the test results of the implementation, proved that the MLP algorithm has a higher value of accuracy than the RBF algorithm. Using the Confusion Matrix, the MLP algorithm has higher value of accuracy with 87.3% than the RBF algorithm with 81.1%. Using the ROC curve, the MLP algorithm also has higher AUC (Area Under the Curve) value with 0.949 than the RBF algorithm with 0.911. Using confusion matrix, the accuracy value of both algorithms are included as good classification, because the AUC value is in the range of 0.80 until 0.90. Using ROC Curve, the accuracy values are included as excellent classification, because the AUC value is in the range of 0.90 until 1.00.
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References
Bramer, M. 2007. Principle of Data Mining. London: Springers-Verlag.
Departemen Kesehatan Republik Indonesia. 2009. Profil Kesehatan Indonesia 2008. Jakarta
Gorunescu, F. 2011. "Data Mining Concepts, Models and Techniques". Berlin Heidelberg: Springer Verlag.
Hananta, I. Y., & Muhammad, H. F. 2011. Dietisien Deteksi Dini & Pencegahan 7 Penyakit Penyebab Mati Muda. Yogyakarta: Media Pressindo.
Hannan, Shaikh Abdul, R. R. Manza, R. J. Ramteke. 2010. Generalized Regression Neural Network and Radial Basis Function for Heart Disease diagnosis. Maharashtra, India. International Journal of Computer Application (0975-8887). Volume 7- No.13.
Kothari, C.R. 2004. Research Methology Methods and Techniques. India: New Age International Limited.
Mahmood, A.M, & Kuppa, M. R. 2010. Early Detection of Clinical Parameters in Heart Disease by Improving Decision Tree Algorithm. 2011 Second Vaagdevi International Conference in Informations Technology for real World Problem,24-28.
Mehdi, Neshat, & Yaghobi, Mehdi. 2009. Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System. Proceeding of the world congress on engineering and computer science 2009,Vol II, WCECS 2009, ISBN:978988-18210-2-7. October 20-22, 2009. pp 1-6
Palaniappan, S. & Awang, R. 2008. Intelligent Heart Disease Prediction System Using Data Mining Technique. IJCSNS International Journal of Computer Science and Network Security. Vol. 8, August 2008, 343350.
Shukla, A., Tiwari, R., & Kala R. 2010. Real Life Applications of Soft Computing. United States of America: CRC Press Taylor and Francis Group.
Sudoyo AW, Setiyohadi B, Alwi I, Simadibrata M, Setiati S. 2009. Buku Ajar Ilmu Penyakit Dalam Jilid II edisi V. Jakarta: Interna Publishing.
Departemen Kesehatan Republik Indonesia. 2009. Profil Kesehatan Indonesia 2008. Jakarta
Gorunescu, F. 2011. "Data Mining Concepts, Models and Techniques". Berlin Heidelberg: Springer Verlag.
Hananta, I. Y., & Muhammad, H. F. 2011. Dietisien Deteksi Dini & Pencegahan 7 Penyakit Penyebab Mati Muda. Yogyakarta: Media Pressindo.
Hannan, Shaikh Abdul, R. R. Manza, R. J. Ramteke. 2010. Generalized Regression Neural Network and Radial Basis Function for Heart Disease diagnosis. Maharashtra, India. International Journal of Computer Application (0975-8887). Volume 7- No.13.
Kothari, C.R. 2004. Research Methology Methods and Techniques. India: New Age International Limited.
Mahmood, A.M, & Kuppa, M. R. 2010. Early Detection of Clinical Parameters in Heart Disease by Improving Decision Tree Algorithm. 2011 Second Vaagdevi International Conference in Informations Technology for real World Problem,24-28.
Mehdi, Neshat, & Yaghobi, Mehdi. 2009. Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System. Proceeding of the world congress on engineering and computer science 2009,Vol II, WCECS 2009, ISBN:978988-18210-2-7. October 20-22, 2009. pp 1-6
Palaniappan, S. & Awang, R. 2008. Intelligent Heart Disease Prediction System Using Data Mining Technique. IJCSNS International Journal of Computer Science and Network Security. Vol. 8, August 2008, 343350.
Shukla, A., Tiwari, R., & Kala R. 2010. Real Life Applications of Soft Computing. United States of America: CRC Press Taylor and Francis Group.
Sudoyo AW, Setiyohadi B, Alwi I, Simadibrata M, Setiati S. 2009. Buku Ajar Ilmu Penyakit Dalam Jilid II edisi V. Jakarta: Interna Publishing.
Published
2014-03-15
How to Cite
Setiadi, A. (2014). KOMPARASI ALGORITMA MULTI LAYER PERCEPTRON DAN RADIAL BASIS FUNCTION UNTUK DIAGNOSA PENYAKIT JANTUNG. Jurnal Pilar Nusa Mandiri, 10(1), 72-80. https://doi.org/10.33480/pilar.v10i1.464
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