Artificial Intellegence Untuk Mendeteksi Penyakit Kelenjar Getah Bening (Lymphadennopathy) Menggunakan Fuzzy Inference System (Fis) Di Kota Batam


Health is a basic thing to be maintained in life, because with a strong health and physical man can run his life. Batam is a rapidly growing city seen from many residents who live in the city of batam, but most of them are less healthy so that the disease is easy to come, another thing that is less his expert in the lymph node and costly in his treatment. The dangerous condition that causes swollen glands is a blood infection. A person suffering from a blood infection will look very weak. And will also experience a fever that will worsen and also accompanied by a body that feels shivering. This infection is caused by a bacterial attack and someone who experienced it should be treated as soon as possible in hospital. The clear-spoken cleavage is part of the human body's defense system. output Solving production problems using the Sugeno and Sugeno fuzzy methods corrects the weaknesses of the pure fuzzy system to add a simple mathematical calculation as part of THEN. In this change, the fuzzy system has a weighted average value (Values) in the IF-THEN fuzzy rules section. The Sugeno fuzzy system also has a disadvantage, especially in the THEN part, that is, by mathematical calculations that it can not provide a natural framework for representing human knowledge in fact. This method uses the mathematical constants or functions of the input variables, and in the defuzzification process uses the centralized mean method.


Aryanto, D., & Pujiyanta, A. (2013). Aplikasi Sistem Pakar Penentuan Asupan Makanan Bagi Penderita Penyakit Gizi Buruk Dengan Inferensi Fuzzy. Jurnal Sarjana Teknik Informatika, 1, 430–439.
[2] Budiman, I., Pratama, M. A., & Sofyan, A. (2017). Fuzzy Dan Dempster-Shafer Pada Sistem Pakar Diagnosa Penyakit Tanaman Cabai. Kumpulan jurnaL Ilmu Komputer, 4(2), 209–222.
[3] Kusumadewi Sri dan Hari Purnomo, (2013). Aplikasi Fuzzy Logic Untuk Pendukung Keputusan, Edisi 2, Graha Ilmu, Yogyakarta.
[4] Meimaharani, R., & Listyorini, T. (2014). Analisis Sisteminference Fuzzy Sugeno Dalam Menentukan Harga Penjualan Tanah Untuk Pembangunan Minimarket. Jurnal SIMETRIS, 5(1), 89–96.
[5] NOV´AK, V. (2016). A Note To Interpretable Fuzzy Models And Their Learning. Iranian Journal of Fuzzy Systems Vol., 13(7), 53–65.
[6] Primartha, R., & Fathiyah, N. (2013). SISTEM PAKAR FUZZY UNTUK DIAGNOSIS KANKER PAYUDARA. Jurnal Generic, 8, 190~197.
[7] Shakiba, A., Hooshmandasl, M. R., Davvaz, B., & Fazeli, S. A. S. (2017). S-approximation spaces: a fuzzy approach. Iranian Journal of Fuzzy Systems, 14(2), 127–154.
[8] Yudanto, A. Y., Apriyadi, M., & Sanjaya, K. (2013). Optimalisasi Lampu Lalu Lintas dengan Fuzzy Logic. Ultimatics, V(2), 58–62.
How to Cite
SESTRI, sestri novia; MAULANA, Algifanri. Artificial Intellegence Untuk Mendeteksi Penyakit Kelenjar Getah Bening (Lymphadennopathy) Menggunakan Fuzzy Inference System (Fis) Di Kota Batam. JURNAL ILMIAH INFORMATIKA, [S.l.], v. 6, n. 01, p. 54-61, mar. 2018. ISSN 2615-1049. Available at: <>. Date accessed: 31 may 2020. doi: