PERBANDINGAN ALGORITMA KLASIFIKASI DATA MINING MODEL C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT DIABETES
Keywords:
Data Mining, Naive Bayes, Decision Tree algorithm C4.5, Diabetes
Abstract
Diabetes is one of the deadly disease, high-risk factors in families that cause diabetes because fat people who do not do physical exercise, and those who do not have a healthy lifestyle and diet excess of what is needed by the body. Based on the history data diabetics can be made on the prediction of diabetes that can help health professionals. Classification is one of data mining techniques that can be used to help predict. Classification can be done with that Decision Tree algorithm C4.5 and Naive Bayes. This study aims to classify and apply data mining classification. Results of data classification in the evaluation using the Confusion Matrix and ROC curve to determine the level of accuracy results using algorithms Decision Tree that is equal to 73.30% and the AUC of the ROC curve was 0733 while the algorithm Naive Bayes amounted to 75.13% AUC values of the ROC curve of 0.810, so it can be said that the algorithm Naive Bayes has the result of a good predictor in predicting diabetes patient.
References
Andriani, Anik (2013). Sistem Prediksi penyakit Diabetes Berbasis Decision Tree. Jurnal Bianglala Informatika Vol. I No. 1 September 2013.
Bramer, M.(2007). Principles of Data Mining London: Springer Clark.
L.A., Kochanska, G., & Ready, R. (2000). Mothers' personality and its interaction with child temperament as predictors of parenting behavior. Journal of Personality and Social Psychology, 79, 274-285.
Gorunescu, F. (2011). Data Mining Concepts, Models and Technique. Berlin: Springer
Han, J., & Kamber, M. (2006). Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman
Hui-Huang, H. (2006). Advanced Data mining Technologies in Bioinformatics. United States of America: Idea Group Publishing.
Jayalakshmi, T., Santhakumaran, A. (2010). Improved Gradient Descent Back Propagation Neural Network for Diagnoses of Type II Diabetes Militus. Global Journal of Computer Science and Technology. Vol.9 Issue 5.
Kahramanli, H., & Allahverdi, N. (2008). Design of A Hybrid System for the Diabetes and Heart Diseases. Expert System with Application, 82-89
Kusrini, & Luthfi, T. E.(2009). Algoritma Data Mining. Yogyakarta: Penerbit Andi
Larose, D. T. (2005). Discovering knowledge in Data. New Jersey: John Willey & Sons, Inc.
Maimon, o., & Rokach, L. (2010). Data Mining and Knowladge Discovery Handbook Second Edition. New York:Springer.
Mehdi Neshat, and Mehdi Yaghobi. (2009, October, 20-22). “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:978-988-18210-2-7. pp 1-6, October 20-22.
Purnama, Parida dan Catur Supriyanto (2013). Deteksi Penyakit Diabetes Type II Dengan Naive Bayes Berbasis Particle Swarm Optimization. Jurnal Teknologi Informasi, Volume 9 Nomor 2, Oktober 2013, ISSN 1414-9999.
Romansyah, F, I.S. Sitanggang, S, & Nurdianti (2009). Fuzzy Decision Tree Dengan Algoritme ID3 Pada Data Diabetes. Internet Working Indonesia Journal Vol. I/No. 2, 2009. University of California Irvine Machine learning Repository. Dikutip 02 November 2015, dari http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/heart/hear.dat
Vercellis, C. (2009). Business Intelligence: Data Mining Optimization for Decision making. United Kingdom: John Wiley & Sons.
Witten, I. H., Frank, E., & Hall, M.A.(2011) Data Mining Practical Machine Learning Tools And Technique. Burlington, Usa: Morgan kaufmann Publishers.
Bramer, M.(2007). Principles of Data Mining London: Springer Clark.
L.A., Kochanska, G., & Ready, R. (2000). Mothers' personality and its interaction with child temperament as predictors of parenting behavior. Journal of Personality and Social Psychology, 79, 274-285.
Gorunescu, F. (2011). Data Mining Concepts, Models and Technique. Berlin: Springer
Han, J., & Kamber, M. (2006). Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman
Hui-Huang, H. (2006). Advanced Data mining Technologies in Bioinformatics. United States of America: Idea Group Publishing.
Jayalakshmi, T., Santhakumaran, A. (2010). Improved Gradient Descent Back Propagation Neural Network for Diagnoses of Type II Diabetes Militus. Global Journal of Computer Science and Technology. Vol.9 Issue 5.
Kahramanli, H., & Allahverdi, N. (2008). Design of A Hybrid System for the Diabetes and Heart Diseases. Expert System with Application, 82-89
Kusrini, & Luthfi, T. E.(2009). Algoritma Data Mining. Yogyakarta: Penerbit Andi
Larose, D. T. (2005). Discovering knowledge in Data. New Jersey: John Willey & Sons, Inc.
Maimon, o., & Rokach, L. (2010). Data Mining and Knowladge Discovery Handbook Second Edition. New York:Springer.
Mehdi Neshat, and Mehdi Yaghobi. (2009, October, 20-22). “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:978-988-18210-2-7. pp 1-6, October 20-22.
Purnama, Parida dan Catur Supriyanto (2013). Deteksi Penyakit Diabetes Type II Dengan Naive Bayes Berbasis Particle Swarm Optimization. Jurnal Teknologi Informasi, Volume 9 Nomor 2, Oktober 2013, ISSN 1414-9999.
Romansyah, F, I.S. Sitanggang, S, & Nurdianti (2009). Fuzzy Decision Tree Dengan Algoritme ID3 Pada Data Diabetes. Internet Working Indonesia Journal Vol. I/No. 2, 2009. University of California Irvine Machine learning Repository. Dikutip 02 November 2015, dari http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/heart/hear.dat
Vercellis, C. (2009). Business Intelligence: Data Mining Optimization for Decision making. United Kingdom: John Wiley & Sons.
Witten, I. H., Frank, E., & Hall, M.A.(2011) Data Mining Practical Machine Learning Tools And Technique. Burlington, Usa: Morgan kaufmann Publishers.
Published
2016-03-15
How to Cite
Fatmawati, F. (2016). PERBANDINGAN ALGORITMA KLASIFIKASI DATA MINING MODEL C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT DIABETES. Jurnal Techno Nusa Mandiri, 13(1), 50-59. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/217
Article Metrics
Abstract viewed = 1964 times
PDF downloaded = 1817 times
Abstract viewed = 1964 times
PDF downloaded = 1817 times
Section
Articles
Copyright (c) 2016 Fatmawati
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.
- The copyright in each article belongs to the author.
- Authors retain all their rights to published works, not limited to the rights set out on this page.
- The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).
- Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);
- The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.
- If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.
