IMPLEMENTASI GREEDY FORWARD SELECTION UNTUK PREDIKSI METODE PENYAKIT KUTIL MENGGUNAKAN DECISION TREE

Authors

  • Fitriyani Fitriyani Universitas ARS
  • Toni Arifin Universitas ARS

DOI:

https://doi.org/10.23887/jstundiksha.v9i1.24896

Keywords:

Penyakit Kutil, Cryotherapy, Immunotherapy, Decision Tree, Greedy Forward Selection

Abstract

Penyakit kutil dapat ditangani dengan berbagai metode seperti cryotherapy dan  immunotherapy, akan tetapi dokter belum mengetahui metode pengobatan yang paling tepat untuk pasien, sehingga diperlukan pengujian agar dapat diketahui metode yang paling tepat untuk pasien. Penelitian ini menggunakan dataset cryotherapy dan immunotherapy dengan menggunakan algoritma klasifikasi Decision Tree. Pada dataset ini terdapat atribut atau fitur yang tidak relevan sehingga dilakukan seleksi fitur menggunakan Greedy Forward Selection. Hasil penelitian ini akan dilakukan perbandingan kinerja dari algoritma Decision Tree tanpa seleksi fitur Greedy Forward Selection dengan Decision Tree yang di integrasikan pada seleksi fitur Greedy Forward Selection dan pemilihan metode pengobatan penyakit kutil yang terbaik.

References

Adeyemo, O. ., & Adeyeye, T. . (2015). Comparative Study of ID3/C4 . 5 Decision tree and Multilayer Perceptron Algorithms for the Prediction of Typhoid Fever. African Journal of Computing & ICT, 8(1), 103–112. https://doi.org/10.15388/ioi.2015.04

Alpaydın Ethem. (2010). Introduction to Machine Learning Second Edition (2nd ed.; T. Dietterich, ed.). London: MIT.

Astarini, I. A., Chappell, A., Scheuring, D., Thompson, S. M., & Miller, C. (2016). Optimasi Metode Cryotherapy untuk Mengeliminasi Virus pada Tunas Kentang In Vitro ( Optimation of Cryotherapy Method to Eliminate Virus on In Vitro Potato Shoot Tips ). J. Hort, (2012).

Bramer, M. (2016). Principles of Data Mining. https://doi.org/10.1007/978-1-84628-766-4

Catal, C., & Diri, B. (2009). A systematic review of software fault prediction studies. Expert Systems with Applications, 36(4), 7346–7354. https://doi.org/10.1016/j.eswa.2008.10.027

Durairaj, M., & Kalaiselvi, G. (2015). Prediction Of Diabetes Using Soft Computing Techniques-A Survey. IInternational Journal Of Scientific & Technology Research, 4(03), 190–192. Retrieved from www.ijstr.org

Gayatri, N., Nickolas, S., & Reddy, A. V. (2010). Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions. Proceedings of the World Congress on Engineering and Computer Science 2010, I.

Gorunescu, F. (2011). Data mining: concepts and techniques. In Springer. https://doi.org/10.1007/978-3-642-19721-5

Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. https://doi.org/10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C

Khozeimeh, F., Alizadehsani, R., Roshanzamir, M., Khosravi, A., Layegh, P., & Nahavandi, S. (2017). An expert system for selecting wart treatment method. Computers in Biology and Medicine, 81(December 2016), 167–175. https://doi.org/10.1016/j.compbiomed.2017.01.001

Laradji, I. H., Alshayeb, M., & Ghouti, L. (2015). Software defect prediction using ensemble learning on selected features. Information and Software Technology, 58, 388–402. https://doi.org/10.1016/j.infsof.2014.07.005

Nugroho, H. W., Adji, T. B., & Setiawan, N. A. (2018). Random Forest Weighting based Feature Selection for C4 . 5 Algorithm on Wart Treatment Selection Method. 8(5), 1858–1863.

Rohman, A., Suhartono, V., & Supriyanto, C. (2017). Penerapan Algoritma C4.5 berbasis Adaboost untuk Prediksi Penyakit Jantung. Jurnal Teknologi Informasi, 13, 13–19.

Sumeet, D., & Xian, D. (2011). Data Mining and Machine Learning in Cybersecurity.

Wahono, R. S. (2015). A Systematic Literature Review of Software Defect Prediction : Research Trends , Datasets , Methods and Frameworks. Journal of Software Engineering, 1(1).

Wu, X., & Kumar, V. (2009). The Top Ten Algorithms in Data Mining (V. Kumar, ed.). Boca Raton, London, New York: CRC Press taylor & Francis Group.

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Published

2020-07-01

How to Cite

Fitriyani, F., & Arifin, T. (2020). IMPLEMENTASI GREEDY FORWARD SELECTION UNTUK PREDIKSI METODE PENYAKIT KUTIL MENGGUNAKAN DECISION TREE. JST (Jurnal Sains Dan Teknologi), 9(1), 76–85. https://doi.org/10.23887/jstundiksha.v9i1.24896

Issue

Vol. 9 No. 1 (2020)

Section

Articles

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