DIAGNOSA STATUS RESIKO JANTUNG KORONER MENGGUNAKAN METODE FUZZY NON STATIONARY

  • Victoria D. V. Latumeten Jurusan Matematika FMIPA Universitas Pattimura
DOI: https://doi.org/10.30598/barekengvol11iss1pp27-38
Keywords: Diagnosa tingkat resiko, fuzzy non-stationary, jantung koroner, mamdani

Abstract

Penyakit jantung koroner (PJK) merupakan penyakit yang apabila sudah terdiagnosa perlu sekali diawasi karena beresiko tinggi terhadap kematian. Resiko kematian tersebut perlu diminimalisasi dengan membuat sebuah media konsultasi dan monitoring terhadap penderita sesuai gejala yang dialami. Dokter dan paramedis adalah pakar yang berkompeten untuk mediagnosa tingkat resiko penyakit jantung koroner, namun dalam pengambilan keputusan yang sulit terdapat keragaman opini pakar (inter-expert) dan seiring waktu opini pakar pun berubah (intra-expert) karena pengetahuannya terus bertambah berdasarkan adanya gejala baru dari penelitian yang dilakukan, kebiasaan baru, atau dipengaruhi keadaan
emosi.
Model fuzzy mengatasi permasalahan ketidakpastian. Model fuzzy yang menggunakan himpunan fuzzy non-stationary (FNIS) mereplikasi keragaman pada manusia sehingga mampu mengatasi permasalahan kegaraman opini pada pakar baik inter-expert maupun intra-expert. Sistem yang dibangun menggunakan model fuzzy non-stationary untuk mendiagnosa tingkat resiko jantung koroner berdasarkan 5 input yakni umur, tekanan darah, gula darah, status BMI, dan kolesterol. Fuzzy sistem pada penelitian ini menggunakan mamdani inferensi. Dua fungsi perturbation function yang digunakan sistem adalah fungsi distribusi acak normal pada variabel input dan fungsi sinusoidal pada variabel output untuk
meng-generate membership function untuk 5 kali perulangan. Hasil perbandingan diagnosa antara pakar, FIS, dan FNIS didapati bahwa FNIS lebih tepat dibandingkan FIS sesuai diagnosa pakar secara manual.

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References

[1] Torres, A. dan Nieto, J. J., 2005, Fuzzy Logic in Medicine and Bioinformatics, Jurnal of Biomedicine and Biotechnology, 1-7.
[2] Garibaldi, Jonathan.M., Jaroszewski, M., and Musikasuwan, S., 2007, New Concepts related to Non-Stationary Fuzzy Sets, Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ:IEEE), London, 1684-1689.
[3] Saleh, A.A.E., Barakat, S. E., and Awad, A.A.E., 2011, A Fuzzy Decision Support System in Management of Breast Cancer, International Journal of Advanced Computer Science and Applications, 2, 34-40.
[4] Visweswaran, S., Cooper, G.F., Angus, D.C., Hsieh, M., Wiesseld, L., and Yealy, D., 2010, Learning patient-specific predictive models from clinical data, Journals of Biomedical Informatics, Elsevier, 43, 669-685.
[5] Cooper, G.F., Alieris, C.F., Ambrosinus R., Aronis, J., Buchanan, B.G., Caruana, R., Fine, M., Glymour, C., Gordon, G., Hanusa, B., Janosky, J.E., Meek, C., Mitchell, T., Richardson, T., Spirtes, P., 1997, An evaluation of machine-learning methods for predicting pneumonia mortality, Artificial Intelligence in Medicine 9, Elsevier, 107-138.
[6] Zhou, S., John, R., Wang, X., dan Garibaldi, J., 2008, Compact fuzzy rules induction and feature extraction using SVM with particle swarms for breast cancer treatments, IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), IEEE, 1469-1475.
[7] Garibaldi, Jonathan .M., Zhou, Shang-Ming, Wang, Xiao-Ying, John, Robert.I and Ellis, Ian.O., 2012, Incorporation of expert variability into breast cancer treatment recommendation in designing protocol guided fuzzy rule system models, Journal of Biomedical Informatics, Elsevier, 45, 447-459.
[8] Coupland, S and Matthews, Stephen G., 2013, Using Nonstationary Fuzzy Sets to Improve the Tractability of Fuzzy Association Rule, Advances in Type-2 Fuzzy Logic, IEEE, 9-14.
[9] Wahyuni, E.G., Widodiprodjo, W., 2013, Prototype Sistem Pakar untuk Mendeteksi Tingkat Resiko Penyakit Jantung Koroner dengan Metode Dempster-Shafer (Studi Kasus: RS. PKU Muhammadiyah Yogyakarta), Berkala MIPA, Vol. 23, pp. 161-171.
[10] Wardhani, R.S., 2014, Aplikasi Sistem Fuzzy Untuk Diagnosa Penyakit Jantung Koroner, Skripsi, Universitas Negeri Yogyakarta, Indonesia.
[11] Musikasuwan, S, 2013, Novel Fuzzy Techniques For Modelling Human Decision Making, Disertasi, School of Computer Science Faculty Science University of Nottingham, Nottingham, Inggris
Published
2017-03-01
How to Cite
[1]
V. Latumeten, “DIAGNOSA STATUS RESIKO JANTUNG KORONER MENGGUNAKAN METODE FUZZY NON STATIONARY”, BAREKENG: J. Math. & App., vol. 11, no. 1, pp. 27-38, Mar. 2017.
Issue
Vol 11 No 1 (2017): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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