PENERAPAN ALGORITMA C4.5 UNTUK PREDIKSI PENYAKIT HEPATITIS
Keywords:
Algorithm C4.5, Data Mining, Hepatitis
Abstract
Hepatitis is an inflammation disease of the liver because an infection that attacks and causes damage to cells and liver function. Hepatitis is a disease precursor of liver cancer. Hepatitis can damage liver function as neutralizing poisons and digestive system in the body that break down nutrients and then spread to all organs of the body that very important for humans. Research on predicting disease hepatitis have been carried out by previous researchers. This research using the method of classification data mining algorithm C4.5. The measurement method using cross-validation, confusion matrix, and ROC curve. The result of this research is a decision tree rule with 77.29% accurate.
References
Bramer, M. (2007). Principles of Data Mining. London: Springer.
Eldin, Ahmed. (2011). A Data Mining Approach for the Prediction of Hepatitis C Virus protease Cleavage Sites.Cairo : International Journal of Advanced Computer Science and Applications Vol 2 No.12.
Gorunescu, Florin. (2011). Data Mining: Concepts and Techniques. Verlag berlin Heidelberg: Springer.
Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques. San Fransisco:
Mofgan Kaufan Publisher.
Karlik. (2011). Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers. Turkey : Journal of Science and Technology Vol. 1 No. 1.
Kumar, Varun & Sharathi, Vijay & Devi, Gayathri (2012). Hepatitis Prediction Model based on Data Mining Algorithm and Optimal Feature Selection to Improve Predictive Accuracy. Vellore : International Journal of Computer Applications (09758887) Volume 51 No. 19.
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Yogyakarta: Andi Publishing.
Larose, D. T. (2005). Discovering Knowledge in Databases. New Jersey: John Willey & Sons Inc.
Liao. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Application . Singapore: World Scientific Publishing.
Myatt, Glenn J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.
Ozyilmaz, Lale & Yildirim, Tulay. 2003). Artificial Neural Network for Diagnosis of Hepatitis Disease.
Riduwan. (2008). Metode dan Teknik Menyusun Tesis. Bandung: Alfabeta.
Santosa, B. (2007). Data Mining Teknik Pemanfaat Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Application of Soft Computing. Taylor and Francis Groups, LLC. UCI (Universitas California, Invene) Machine Learning Repositorydengan alamat website http://archive.ics.uci.edu/ml/machine-learningdatabases/hepatitis/ Akses : 5 Januari 2013 pukul 10:00
Vercellis, C. (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate: John Willey & Sons Inc.
Witten, H. I., Eibe, F., & Hall, A. M. (2011). Data Mining Machine Learning Tools and Techiques. Burlington: Morgan Kaufmann Publisher.
Wu, X., & Kumar, V. (2009). The Top Ten Algorithms in Data Mining. Boca Raton: CRC Press.
Eldin, Ahmed. (2011). A Data Mining Approach for the Prediction of Hepatitis C Virus protease Cleavage Sites.Cairo : International Journal of Advanced Computer Science and Applications Vol 2 No.12.
Gorunescu, Florin. (2011). Data Mining: Concepts and Techniques. Verlag berlin Heidelberg: Springer.
Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques. San Fransisco:
Mofgan Kaufan Publisher.
Karlik. (2011). Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers. Turkey : Journal of Science and Technology Vol. 1 No. 1.
Kumar, Varun & Sharathi, Vijay & Devi, Gayathri (2012). Hepatitis Prediction Model based on Data Mining Algorithm and Optimal Feature Selection to Improve Predictive Accuracy. Vellore : International Journal of Computer Applications (09758887) Volume 51 No. 19.
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Yogyakarta: Andi Publishing.
Larose, D. T. (2005). Discovering Knowledge in Databases. New Jersey: John Willey & Sons Inc.
Liao. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Application . Singapore: World Scientific Publishing.
Myatt, Glenn J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.
Ozyilmaz, Lale & Yildirim, Tulay. 2003). Artificial Neural Network for Diagnosis of Hepatitis Disease.
Riduwan. (2008). Metode dan Teknik Menyusun Tesis. Bandung: Alfabeta.
Santosa, B. (2007). Data Mining Teknik Pemanfaat Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Application of Soft Computing. Taylor and Francis Groups, LLC. UCI (Universitas California, Invene) Machine Learning Repositorydengan alamat website http://archive.ics.uci.edu/ml/machine-learningdatabases/hepatitis/ Akses : 5 Januari 2013 pukul 10:00
Vercellis, C. (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate: John Willey & Sons Inc.
Witten, H. I., Eibe, F., & Hall, A. M. (2011). Data Mining Machine Learning Tools and Techiques. Burlington: Morgan Kaufmann Publisher.
Wu, X., & Kumar, V. (2009). The Top Ten Algorithms in Data Mining. Boca Raton: CRC Press.
Published
2014-03-15
How to Cite
Septiani, W. (2014). PENERAPAN ALGORITMA C4.5 UNTUK PREDIKSI PENYAKIT HEPATITIS. Jurnal Techno Nusa Mandiri, 11(1), 69-78. https://doi.org/10.33480/techno.v11i1.172
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