Diagnosis Expert System Using Bayes Algorithm

  • Khoironi MTI Universitas Amikom Yogyakarta
  • Abdul Rosyid MTI Universitas Amikom Yogyakarta
  • Muhammad Azmi STMIK Syaikh Zainuddin Nahdlatul Wathan Anjani
Keywords: Expert system, kidney disease, bayes

Abstract

Expert systems are computer-based systems that use knowledge, facts and reasoning techniques to solve problems that usually only an expert can solve in a particular field. Expert systems provide added value to technology to assist in an increasingly sophisticated information age. This expert system application produces output in the form of possible kidney disease based on the symptoms felt by the user. This system uses the Bayes method.

The system will look for the highest probability value, from various possible types of disease based on the information requested by the user and exclude the user. The system for diagnosing kidney disease is determined by determining the type of disease based on the user's symptoms. The resulting Bayes value is between 0 and 1. If the resulting Bayes value is continuous, the higher the certainty that the disease is affected. In fact, if the resulting Bayes value is continuous, the less certain the disease involved is.

References

[1] Fathansyah, Basis Data, Bandung: Informatika, 1999.
[2] N. S. Sri Mulyani, “Peranan Model Pengembangan System Development Life Cycle (SDLC) Terhadap Kualitas Sistem Informasi,” 2009.
[3] A. A. Ziya, Structured Analysis and Design of Information System, New Jersey: Pretice Hall, 2987.
[4] Mitchell R.N., Buku Saku Dasar Patologis Penyakit Robbin & Cotran, Jakarta: EGC, 2006.
[5] H. Haewook, A. M. Segal, J. L, Seifter and J. T. Dwyer, “Management Of Kidney Stones (Nephrolithiasis),” Jurnal clinic Noutritional, pp. 137-152, 2015.
[6] Al Fatta, Analisis dan Perancangan Sistem Informasi, Yogyakarta: Andi Offset, 2007.
[7] Sutabri, Sistem Informasi Manajemen, Yogyakarta: ANDI, 2004.
[8] S. Rosa, Rekayasa Perangkat Lunak Terstruktur dan Berorientasi Objek, Bandung: Informatika, 2013.
[9] T. Amor, Sistem Informasi Akuntansi Pengembangan Sistem, Yogyakarta: Bahtera Mas, 2017.
[10] M. A. Prof. Dr. Jogiyanti HM, Analisa dan Desain Sistem Informasi: Pendekatan Teori dan Praktek Aplikasi Bisnis, Andi Offset, 2005.
[11] H. Sismoro, Pengantar Logika Informatika Algoritma dan Pemrograman Komputer, Yogyakarta: Andi Offset, 2005.
[12] M. Arhami, Konsep Dasar Sistem Pakar, Yogyakarta: ANDI, 2005.
[13] E. Turban, Decicion Support System and Expert System, USA: Prentice Hall International Inc., 1995.
[14] R. Kenward and C. K. Tan, Penggunaan Obat Pada Gangguan Ginjal, dalam Aslam Farmasi Klinis: Menuju Pengobatan Rasional dan Penghargaan Pilihan Pasien, Jakarta: PT. Elex Media Kumputindo Gramedia, 2003.
[15] E. Sukandar, “Infeksi Saluran Kemih Pasien Dewasa,” Buku Ajar Ilmu Penyakit Dalam, Jilid 1, Jakarta, FK UI. 2004, pp. 553-557.
[16] D. Mary, D. Jackson and J. Keogh, Keperawatan Medikal Bedah, Yogyakarta: Salemba Medika, 2014.
[17] Dorland, Kamus Kedokteran, Jakarta: EGC, 2002.
[18] T. R. V. HEALTH. in Gagal Ginjal (Infromasi Lengkap Untuk Penderita dan Keluarganya), Jakarta, PT Gramedia Pustaka Utama, 2008, p. l.
[19] S. Djoko, “Angka Kejadian Sakit Ginjal di Indonesia,” 2008.
[20] L. Depkes, “Klipping Ginjal,” 2006.
[21] V. R. Dewi, “Perancangan Aplikasi Mobile Sistem Pakar Diagnosis Penyakit Dengan Gejala Demam Berbasis Android,” 2014.
Published
2021-01-02
How to Cite
Khoironi, Abdul Rosyid, & Muhammad Azmi. (2021). Diagnosis Expert System Using Bayes Algorithm. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 1(2), 39-44. https://doi.org/10.46764/teknimedia.v1i2.24
Abstract viewed = 348 times
PDF downloaded = 775 times