Diagnosis of Tuberculosis by Artificial Neural Network Algorithm

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Tanggal

2019-04-01

Penulis

Abstraksi

Tuberculosis is an infectious disease caused by a bacterium called Mycobacterium tuberculosis and is the highest cause of death that occurs in productive age 15-50 years, weak economic groups, and low educated. In this study, the author will apply the data mining classification method, namely the Artificial Neural Network Algorithm to diagnose tuberculosis. Based on the results of the performance measurement of the model using Cross Validation, Confusion Matrix and ROC Curve testing methods, it is known that artificial neural network algorithms have an accuracy rate of 89.89% and area under the curva (AUC) value of 0.975. This shows that the resulting model including the classification category is very good because it has an AUC value between 0.90-1.00. 

URI
http://jurnal.polgan.ac.id/index.php/sinkron/article/view/10028/206

Bidang ilmu
Data Mining

Bibliografi

Alpaydin, E. (2010). Introduction to Machine Learning. London: The MIT Press. 

Amrin, A. (2018). Perbandingan Metode Neural Network Model Radial Basis Function Dan Multilayer Perceptron Untuk Analisa Risiko Kredit Mobil.  Jurnal Paradigma, XX(1),  31–38.  Retrieved from https://ejournal.bsi.ac.id/ejurnal/index.php/paradigma/article /view/2783 

Amrin, A., & Saiyar, H. (2018). Aplikasi Diagnosa Penyakit Tuberculosis Menggunakan Algoritma Naive Bayes.  Jurnal Jurikom,  5(5), 498–502. Retrieved from https://ejurnal.stmik-budidarma.ac.id/index.php/jurikom/article/view/900/864 

Fine, J. (2012).  An Overview Of Statistical Methods in Diagnostic Medicine. Chapel Hill. 

Gorunescu, F. (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer. 

Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques.  Soft Computing  (Vol. 54). San Fransisco: Morgan Kauffman. https://doi.org/10.1007/978-3-642-19721-5 

Kusumadewi, S. (2010). Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Teknik Informatika FT UII. 

Larose, D. . (2005). Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc. 

Liao, T. W. (2007).  Recent Advances in Data Mining of Enterprise Data: Algorithms and Application.  Singapore: World Scientific Publishing. 

Maimon, O., & Rokach, L. (2010).  Data Mining And Knowledge Discovery Handbook. New York: Springer. 

Myatt, G. J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc. 

Orhan, E., Temurtas, F., & Tanrıkulu, A. Ç. (2010). Tuberculosis Disease Diagnosis Using Artificial Neural Networks. Springer, 299-302. 

Santosa, B. (2007). Data Mining Teknik Pemanfaatan Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu. 

Sogala, S. S. (2006). Comparing the Efficacy of the Decision Trees with Logistic Regression for Credit Risk Analysis. India. 

Sumathi, S., & Sivanandam, S. N. (2006).  Introduction to Data Mining and its Applications. Berlin Heidelberg New York: Springer. 

Vercellis, C. (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd. 

Widoyono. (2011).  Penyakit Tropis Epidemiologi, Penularan, Pencegahan dan Pemberantasan.  Jakarta: Erlangga. 

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning and Tools. Burlington: Morgan Kaufmann.