Aplikasi Fuzzy Logic dalam Diagnosa Penyakit Diabetes Mellitus pada PUSKESMAS di Jakarta Timur

Penulis

  • Zaimatun Niswati Teknik Informatika, Universitas Indraprasta PGRI
  • Aulia Paramita Teknik Informatika, Universitas Indraprasta PGRI
  • Fanisya Alva Mustika Teknik Informatika, Universitas Indraprasta PGRI

DOI:

https://doi.org/10.25077/TEKNOSI.v2i3.2016.21-30

Kata Kunci:

Fuzzy Inference System (FIS) Mamdani, Matlab, Graphical User Interface (GUI), Diabetes Mellitus

Abstrak

Abstract— Patients with diabetes mellitus increased from year to year. This is due to delays in diagnosis of the disease and also because of unhealthy lifestyles. This study aims to create an application of decision support systems in the field of health, namely the diagnosis of the disease Diabetes Mellitus with Fuzzy Inference System (FIS) Mamdani, so that a layman can perform early diagnosis and immediate treatment. Decision Support System Techniques developed to improve the effectiveness of decision-makers. Samples are six puskesmas in East Jakarta. This application uses five variables as inputs consisting of glucose 2 hours after a meal, Diastolic blood pressure, body mass index, family history of diabetes, total pregnancies and one variable as output. The data obtained be processed using fuzzy logic approach to programming Matlab and made Graphical User Interface (GUI). Result is an expert system for diagnosis of Diabetes Mellitus .The trial results by midwives and nurses puskesmas is 100% of these applications in accordance with the doctorâ€s diagnosis. It is to help improve the quality of service in the Puskesmas in East Jakarta, thus satisfying the users and puskesmas be able to compete both nationally and internationally. 

Biografi Penulis

Zaimatun Niswati, Teknik Informatika, Universitas Indraprasta PGRI

Teknik InformatikaUniversitas Indraprasta PGRI

Referensi

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Unduhan

Telah diserahkan

30-09-2016

Diterima

24-10-2016

Diterbitkan

14-12-2016

Cara Mengutip

[1]
Z. Niswati, A. Paramita, dan F. A. Mustika, “Aplikasi Fuzzy Logic dalam Diagnosa Penyakit Diabetes Mellitus pada PUSKESMAS di Jakarta Timur”, TEKNOSI, vol. 2, no. 3, hlm. 21–30, Des 2016.

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