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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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KOMPARASI METODE KLASIFIKASI DATA MINING ALGORITMA C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT HEPATITIS
Keywords:
Data Mining, Machine Learning, Algoritma C45, Naive Bayes
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 of predicting disease hepatitis have been carried out by previous researchers. This research using the method of classification data mining algorithm C4.5 and Naïve Bayes is then performed comparative to both methods., The measurement of two methods using cross-validation, confusion matrix, and ROC curve. The result of this research is the best algorithm that can be used to predict disease hepatitis.
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References
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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 (0975-8887) 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 Repository dengan alamat website http://archive.ics.uci.edu/ml/machine-learning-databases/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
2017-03-15
How to Cite
Septiani, W. (2017). KOMPARASI METODE KLASIFIKASI DATA MINING ALGORITMA C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT HEPATITIS. Jurnal Pilar Nusa Mandiri, 13(1), 76-84. https://doi.org/10.33480/pilar.v13i1.149
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