DOI: https://doi.org/10.21456/vol5iss2pp98-108

Studi Implementasi Adaptive Neuro Fuzzy Inference System Untuk Menentukan Normalitas Kehamilan

*Lili Rusdiana  -  STMIK Palangkaraya, Kalimantan Tengah, Indonesia
Eko Sediyono  -  Magister Sistem Informasi Universitas Kristen Satya Wacana, Indonesia
Bayu Surarso  -  Jurusan Matematika, Fakultas Sains dan Matematika Universitas Diponegoro, Indonesia

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Abstract

Early detection of normality pregnancy is one of the ways to prevent more serious disorders in pregnancy. This thesis study the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the normality of pregnancy. The period of pregnancy and complaints during pregnancy are used as inputs and the normality of pregnancy as output. Data were analyzed using ANFIS method and using Sugeno FIS rules. The program simulation results show that the performance of ANFIS can be implemented to determine the normality of pregnancy. The learning results on different training with the highest level of accuracy of 77,5% can recognize the symptoms and 97.5% could identify the diagnosis to determine the normality of pregnancy. The system can provide the necessary information about the normality of pregnancy. The results show that ANFIS can be used to determine the normality of pregnancy.

 

 

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Keywords: ANFIS; Normality of Pregnancy

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Section: Research Articles
Language : EN
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  1. Adnan, M.R.H.M., Azlan M.Z., and Habibollah H., 2011. Consideration of Fuzzy Components for Prediction of Machining Performance: A Review. Procedia Engineering 24, 754-758
  2. Alayon, S., Richard R., Simon K.W. and Juan R., 2007. A fuzzy system for helping medical diagnosis of malformations of cortical development. Journal of Biomedical Informatics 40, 221–23
  3. Al-Hmouz, A., Jun S., Senior M., Rami A. and Jun Y., 2012. Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning. IEEE Transactions On Learning Technologies 5, 226-237
  4. Alparslan, E., Adem K., Hayretdin B., 2011. Classification of confidential documents by using adaptive neurofuzzy inference systems. Procedia Computer Science 3, 1412–1417
  5. Ditjen Dikti Kemdikbud, 2011. Draft Standar Kompetensi Bidan Indonesia
  6. Faisal, T., Mohd N.T., dan Fatimah I., 2012. Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients. Expert Systems with Applications 39, 4483–4495
  7. Fang, H., Craig J., Cristian S., and Kimberly A.E., 2011. Neurotoxicology and Teratology, A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering, Neurotoxicology and Teratology 33, 155-165
  8. Giarattano, J.C., and Riley, G., 1994. Expert System : Principles and Programming, 2nd edition, PWS Publishing, USA
  9. Kusmiyati, Y., Wahyuningsih, H.P., Sujiyatini, 2009. Perawatan Ibu Hamil, Yogyakarta, Fitramaya
  10. Kusrini, 2008. Aplikasi Sistem Pakar, Yogyakarta, Andi
  11. Kusumadewi, S., Sri H., Agus, H., dan Retantyo, W., 2006. Fuzzy Multi-Attribute Decision Making, Yogyakarta, Graha Ilmu
  12. Minardi, J., 2014. Sistem Pakar Untuk Diagnosis Penyakit Kehamilan Menggunakan Metode Dempster-Shafer, Tesis Magister Sistem Informasi, Universitas Diponegoro, Semarang
  13. Shelly, G.B., and Rosenblatt, H.J., 2012. Analysis and Design For Systems, 9th edition, Cengage Learning, International edition
  14. Sulistyawati, A., 2009. Asuhan Kebidanan Pada Masa Kehamilan, Jakarta, Salemba Medika
  15. Utami, S., 2008. Info Penting Kehamilan, Jakarta, Dian Rakyat
  16. Vanajakshi, L., and Rillet, L.R., 2004. A Comparison of The Performance of Artificial Neural Network and Support Vector Machines for The Prediction of Traffic Speed, IEEE Intelligent Vehicles Symposium, 14-17
  17. Vladan, P., Nenad R., and Vladimir P., 2009. Identification of sport talents using a web-oriented expert system with a fuzzy module, Expert Systems with Applications 36, 8830–8838
  18. Wang, G., Jinxing H., Jian M., and Lihua H., 2010. A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering, Expert Systems with Applications 37, 6225–6232
  19. Widodo, T.S., 2005. Sistem Neuro Fuzzy untuk Pengolahan Informasi, Pemodelan, dan Kendali, Yogyakarta, Graha Ilmu
  20. Zeki T.S., Mohammad V.M., Yousef A., Talayeh T., 2012. An Expert System for Diabetes Diagnosis, American Academic and Scholarly Research Journal 4
  21. Zhou Q., Yuxiang W., Christine W., and Paitoon T., 2011. From neural network to neuro-fuzzy modeling: applications to the carbon dioxide capture process. Energy Procedia 4, 2066–2073

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