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All Journal Jurnal Simantec Sinkron : Jurnal dan Penelitian Teknik Informatika IJEEIT : International Journal of Electrical Engineering and Information Technology EDUMATIC: Jurnal Pendidikan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Pengabdian kepada Masyarakat Nusantara SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Ngabdimas : Pengabdian Pada Masyarakat J-Icon : Jurnal Komputer dan Informatika Jurnal Pengabdian Kepada Masyarakat GEMBIRA (Pengabdian Kepada Masyarakat) Ngabdimas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Yadi
Institut Teknologi Pagar Alam
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Journal : J-Icon : Jurnal Komputer dan Informatika

 1 
EXPERT SYSTEM DIAGNOSIS PENYAKIT GINJAL MENGGUNAKAN FORWARD CHAINING Yadi Yadi
J-ICON : Jurnal Komputer dan Informatika Vol 10 No 2 (2022): Oktober 2022
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v10i2.8187

Abstract

Kidney disease is a disease that is often experienced by people aged over 50 years. Several factors that cause kidney disease include hypertension, diabetes, obesity, smoking and family history of kidney disease sufferers. As many as 42,000 people per year die from kidney disease. Sufferers in Indonesia, which is estimated by the Ministry of Health, based on the 2013-2018 Basic Health Research (Riset Kesehatan Dasar (RIKESDA)) the number of patients experiencing kidney disease symptoms continues to grow to reach 739,208 people in 2013 while chronic kidney sufferers. It was recorded that in 2020 BPJS Kesehatan spent a large enough budget for kidney failure patients as many as 1,763,260 patients. The high number of patients with kidney disease was due to a lack of physical activity and consumption of healthy foods and fruits and vegetables. Therefore, it is necessary to detect as early as possible to prevent the onset of kidney disease. Technology is one of the disseminations of good information in handling kidney disease. This study aims to build an expert system for diagnosing kidney disease. The results of the research on an expert system for diagnosing kidney disease based on a website that can be accessed by the public by providing information on the detection of kidney disease symptoms with 27 symptoms. The process of tracking symptoms using forward chaining. Conclusion tests carried out using black box and usability testing have been running well, as seen from the input and output functionality on the system used by the user. In addition, the expert system helps in providing information to the public to detect the symptoms of kidney disease as early as possible.
Co-Authors Agung Saputra Alharia Dinata Alharia Dinata, Alharia Apriana Asminah Asminah Buhori Muslim Debi Guzmaliza Desi Puspita Inka Rizki Padya Irma Sendy Aristya Madi Harta Mariana Purba Mariana Purba Masagus Taswin Panyahuti Panyahuti Putri Ayu Lestari Rahayu, Chika Risnaini Masdalipa Siti Aminah Siti Aminah Siti Aminah Supriyanto Wisdalia Maya Sari Wita Hariani Yuli Eprianti Yulpa Raberta