Optimization Neuro Fuzzy Using Genetic Algorithm For Diagnose Typhoid Fever

Muhamad Nasrul Fata, Riza Arifudin, Budi Prasetiyo

Abstract


Neuro Fuzzy is one method in the field of information technology used in diagnosing an disease. The application of Neuro Fuzzy is to identify disease. Genetic algorithms can be used to find solutions without paying attention to the subject matter specifically, one of which is an optimization problem. Typhoid or typhoid fever is a disease caused by Salmonella enterica bacteria, especially its derivatives. The diagnosis of typhoid fever is not an easy thing to do. This is because some of the indications experienced by patients also appear in other diseases. The number of patients with typhoid fever that requires accuracy in diagnosing typhoid fever based on indications caused. Based on this background this study aims to assist in the diagnosis of typhoid fever with 11 indication variables. This study uses medical record data for typhoid fever in 2017 Tidar Magelang Hospital. The method used is Neuro Fuzzy which optimizes the value of the degree of membership with genetic Algorithms. Then the value of the degree of neuro fuzzy membership is more optimal. The results of this optimization are the diagnosis of typhoid fever based on the variable of indications entered. From the research results obtained from the neuro fuzzy method get an 80% accuracy value and neuro fuzzy optimization results with genetic algorithms with a value of pc 0.5, pm 0.2 and max generation 25 the value of accuracy increases to 90%. Suggestions from this study, need to add more specific indication variables.

Keywords


Neuro Fuzzy, Genetic Algorithm, Typhoid Fever, Diagnosis, Optimization, Accuration

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References


Alamsyah & Muna, I H. (2016). Metode Fuzzy Inference System untuk PeValuean Kinerja Pegawai Perpustakaan and Pustakawan. Scientific Journal of Informatics. Vol. 3(1): 88-98.

Castillo, O., Melin, P., Kacprzyk, J., & Pedrycz, W. (2007). Type-2 Fuzzy Logic: Theory and Applications, in Granular Computing,. Granular Computing, 2007. GRC 2007. IEEE International Conference on, 2007, pp. 145-145.

Muzakir, M., Farmadi, A., & Indriani, F. (2017). Implementasi Algoritma Neuro-Fuzzy Untuk Diagnosa Penyakit Skizofrenia. Jurnal Elektronik Nasional Teknologi and Ilmu Komputer (JENTIK), 219-231.

Hani'ah, U., Arifudin, R., & Sugiharti, E. (2016). Implementasi Adaptive Neuro-Fuzzy Inference System (Anfis) untuk Peramalan Pemakaian Air di Perusahaan Daerah Air Minum Tirta Moedal Semarang. Scientific Journal of Informatics. Vol. 3(1): 76-87.

Kurniawati, D. O., Hidayat, R., & Hantono, B. S. (2014). Diagnosis Penyakit Pasien Menggunakan Sistem Neuro Fuzzy. Seminar Nasional Teknologi Informasi and Komunikasi 2014 (SENTIKA 2014), pp. 412-418.

Indrianingsih, Y. (2010). Algoritma Genetik Untuk Menyelesaikan Masalah Optimasi Fungsi Berkendala dengan Pengkodean Bilangan Bulat. Vol. 2(1): 67-76.

Muttaqin, A., & Sari, K. (2011). Gangguan Gastrointestinal:Aplikasi Asuhan Keperawatan Medical Bedah. Jakarta: Salemba Medika.

Inawati. (2014). Fever Tifoid. Jurnal Universitas Wijaya Kusuma Surabaya, 1-6.

Organization, W. (2003). Background document : The Diagnosis, treatment and prevention of typhoid fever. Geneva: World Health Organization.

Parry, C., Hien, T., White, N., & Farrar, J. (2002). Typhoid Fever. New England Jounal of Medicine, 1770-1782.

Gaind, R., Paglietti, B., Murgia, M., Dawar, R., Uzzau, S., Cappuccinelli, P., Rubino, S. (2006). Molecular characterization of ciprofloxacin-resistant Salmonella enterica serovar Typhi and Paratyphi A causing enteric fever in India. Journal of Antimicrobial Chemotherapy, 1139-1144.

Hatta, M., Bakker, M., Beers, S. v., Abdoel, T. H., & Smiths, H. L. (2009). Risk Factors for Clinical Typhoid Fever in Rural South Sulawesi, Indonesia. International Journal of Tropical Medicine, 4, 91-99.

Slamet, J. (2002). Kesehatan Lingkungan. Yogyakarta: Gadjah Mada University Press.

Widoyono. (2008). Penyakit Tropis: Epidemiologi, Penularan, Pencegahan and Pemberantasannya. Semarang: Erlangga.




DOI: https://doi.org/10.15294/sji.v6i1.17097

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