Eka Wulansari Fridayanthie


In the case of hepatitis disease prediction has been solved by a method using Support Vector Machine (SVM) .Penyakit hepatitis is an inflammatory disease of the liver due to viral infection that attacks and cause damage to cells and organs function hati.Penyakit forerunner hepatitis is a disease of the liver cancer. Attributes or variables that have as many as 20 attributes which consists of 19 attributes preditor and 1 as the output destination attribute used to differentiate the results of the examination. Invene dataset from the University of California (UCI) Machine Learning Repository 583 as the data used and replace missing after the data is used only to evaluate the data 153 SVMyang approach proposed in the study ini.Hasil simulations showed that by developing this model achieved a reduction in dimensions and identification hati.Salah cancer of the optimization algorithm is quite popular is Naïve Bayes. In this study, will be used also classification algorithm Support Vector Machine (SVM) will be used to establish a predictive classification model of hepatitis.

Keywords: Hepatitis,Naïve Bayes , Support Vector Machine

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Ansari, U., Soni, S., Soni, J., & Sharma, D. (2011). Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Application , 43-48.

Aydin, I., Karakose, M., & Akin, E. (2011). A multi-objective artificial immune algorithm for parameter optimization in support vector machine.Computer Engineering Department , 120-129.

Badrul, Mohammad (2012). Prediksi Hasil Pemilu Legislatif Dki Jakarta Dengan Metode Neural Network Berbasis Particle Swarm Optimization Tesis, Magister Ilmu Komputer,STMIK Nusa Mandiri, Jakarta

Dong, Y., Xia, Z., Tu, M., & Xing, G. (2007). An Optimization Method For Selecting Parameters In Support Vector Machines. Sixth International Conference On Machine Learning And Applications , 1.

Handayanna,Frisma (2012). Penerapan Particle Swarm Optimization Untuk Seleksi Atribut Pada Metode Support Vector Machine Untuk Prediksi Penyakit DiabetesTesis, Magister Ilmu Komputer,STMIK Nusa Mandiri,Jakarta

Huang, K., Yang, H., King, I., & Lyu, M. (2008).Machine Learning Modeling Data Locally And Globally. Berlin Heidelberg: Zhejiang University Press, Hangzhou And Springer-Verlag Gmbh.

Larose, D. T. (2005).Discovering Knowledge in Data an Introduction to Data Mining.New Jersey: John Wiley & Sons, Inc., Hoboken.

Lasut, Desiyanna (2012). Prediksi Loyalitas Pelanggan Pada Perusahaan Penyedia Layanan Multimedia Dengan Algoritma C4.5 Berbasis Particle Swarm Optimization Tesis,Program Studi Teknik Informatika Program Pasca Sarjana Magister Komputer,STMIK Eresha,Jakarta

Maimon, O. (2010). Data Mining And Knowledge Discovery Handbook. New York Dordrecht Heidelberg London: Springer.

Masripah, Siti (2011). Algoritma klasifikasi c4.5 berbasis particle swarm optimization untuk evaluasi penentuan kelayakan pemberian kredit Koperasi syariah Tesis, Magister Ilmu Komputer,STMIK Nusa Mandiri,Jakarta

Septiani, Dwi Wisti (2013). Analisa Dan Komparasi Metode Klasifikasi Data Mining Algoritma C4.5, Naïve Bayes,Dan Neural Network Untuk Prediksi Penyakit Hepatitis Tesis, Magister Ilmu Komputer,STMIK Nusa Mandiri,Jakarta

Salappa, A., Doumpos, M., & Zopounidis, C. (2007). Feature SelectionAlgorithms in Classification Problems: An Experimental Evaluation. SystemsAnalysis, Optimization and Data Mining in Biomedicine , 199-212.

Park, T. S., Lee, J. H., & Choi, B. (2009).Optimization for Artificial NeuralNetwork with Adaptive inertial weight of particle swarm optimization.CognitiveInformatics, IEEE International Conference , 481-485.

Rinawati (2012).Penerapan Particle Swarm Optimization Untuk Seleksi Atribut Pada Metode Support Vector Machine Untuk Penentuan Penilaian Kredit Tesis, Magister Ilmu Komputer,STMIK Nusa Mandiri,Jakarta

Sousa, T., Silva, A., & Neves, A. (2004). Particle Swarm Based Data Mining Algorithms for Classification Tasks. Parallel Computing , 30, 767-783.

Witten, I. H., Eibe, F., & Hall, M. A. (2011).Data Mining: Practical Machine Learning Tools and Techniques 3D Edition. United State.

X. Hu, R. Eberhart, and Y. Shi. Recent advances in particle swarm, , IEEE Congress on Evolutionary Computation 2004, Portland, Oregon, USA

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