Diagnosis of Tuberculosis By Artificial Neural Network Algorithm

Main Article Content

Amrin Amrin

Abstract

Sangat penting bagi dokter untuk melakukan diagnosa secara dini penyakit tuberculosis agar dapat mengurangi penularan penyakit tersebut kepada masyarakat luas.  Pada penelitian ini, penulis akan menerapkan metode klasifikasi data mining, yaitu Algoritma Jaringan Syaraf Tiruan untuk mendiagnosa penyakit tuberculosis. Berdasarkan hasil pengukuran performa dari model tersebut dengan  menggunakan  metode pengujian Cross Validation, Confusion Matrix dan Kurva ROC, diketahui bahwa algoritma jaringan syaraf tiruan memiliki tingkat akurasi sebesar 89,89% dan nilai area under the curva (AUC) sebesar 0,975. Hal ini menunjukkan bahwa model yang dihasilkan termasuk katagori klasifikasi  sangat baik karena memiliki nilai AUC antara 0.90-1.00.

Downloads

Download data is not yet available.

Article Details

How to Cite
AMRIN, Amrin. Diagnosis of Tuberculosis By Artificial Neural Network Algorithm. SinkrOn, [S.l.], v. 3, n. 2, p. 223-228, mar. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10028>. Date accessed: 24 may 2019. doi: https://doi.org/10.33395/sinkron.v3i2.10028.
Section
Articles
**************** Abstract viewed = 29 times ****************

References

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

[2] 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

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

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

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

[6] 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

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

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

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

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

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

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

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

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

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

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

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

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