leading to death . People suffering from this disease are unaware that they suffer from diabetes. early detection is required in order to avoid the risk of more severe complications and risk of death. Today many data mining applications are applied to solve problems in the medical world. The ability of data mining is very important that one of them is generating the prediction and classification, the ability to make a prediction and classification makes data mining became popular in the world of health. Results obtained from the processing of data mining can be used as new knowledge to predict adverse health events in the future that can be overcome. In this research will be to improve the accuracy of the method optimization value nerutal network using genetic algorithm optimization method. The value of diabetes dataset processing accuracy by using a neural networkt is 74.46% while the value of accuracy by using a neural network optimization algorithm genetich is 77.10%.

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Elma, kolce(cela) dan Neki, Frasheri. (2012). “A Literature Review of Data Mining Techniques used in Healthcare Databases”, ICT Innovations Web Proceedings - Poster Session ISSN 1857-7288.

Han, J & Kamber. (2007). “Data Mining Concepts, Models and Techniques ”, Second Edition, Morgan Kaufmann Publisher, Elsevier.

International Diabetes Federation. (2012). IDF Diabetes Atlas 5th ed, Brussels: International Diabetes Federation, www.idf.org/diabetesatlas. Accessed December 3, 2014.

Joseph L. Breault. (2012). “Data Mining Diabetic Databases:Are Rough Sets a Useful Addition”.

Karthikeyani, V dkk (2012). “Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction”, International Journal of Computer Applications (0975 – 8887) Volume 60– No.12 hal 21-26.

Larose, Daniel T. (2005). “Discovering Knowledge in Data An Inntrodution Data Mining”,Wiley-Interscience, New Jersey, USA.

Maimon, O., Rokach, L. (2010). “Data Mining and Knowledge Discovery Handbook 2nd Ed “ ,Springer, Berlin.

Maimon, Oded & Rokach, Lior (2005), “Data Mining and Knowledge Discovery Handbook”, Springer, New York

Myatt, Glenn J (2007), “ Making sense of data: A Practical Guide to Exploratory data analysis and Data Mining”, John Wiley & Sons Inc, New Jersey.Perkumpulan Endokrinologi Indonesia (PERKENI). (2006). “Diabetes Mellitus Konsensus Pengelolaan dan Pencegahan DM tipe 2 di Indonesia”, Jakarta

Pusat data dan informasi Kemenkes RI. (2012). “Penyakit Tidak Menular, Buletin Jendela data dan Informasi Vol 2”, ISSN 2008-207x hal 1-13, Jakarta

Sanakal, Ravi., Jayakumari, T Smt. (2014). “Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine”, International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 2, ISSN: 2231-2803 hal 94-98.

Sivandam S.N, Sumathi, S. (2006). “Intodution to Data Mining and Its Application”, Berlin, Springer.

Tjokroprawiro, A. (2006). “Hidup Sehat dan Bahagia Bersama Diabetes Melitus”, Jakarta: PT Gramedia Pustaka Utama

Vercellis,C. (2009). “Business Intelligence : Data Mining and Optimization for Decision Making, Wiley

Whitcombe, J.M., Cropp, R.A., Braddock, R.D., Agranovski, I.E. (2006). ”The use of sensitivity analysis and genetic algorithms for the management of catalystemissions from oil refi neries” Math. Comput. Model. 4 4, 430 e 438

DOI: https://doi.org/10.31294/p.v17i2.751


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