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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Pengabdian Masyarakat IPTEK sudo Jurnal Teknik Informatika Jurnal Sistem Komputer Triguna Dharma (JURSIK TGD) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
Ardianto Pranata
STMIK Triguna Dharma
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Penerapan Metode Hybrid Case Base Pada Sistem Pakar Diagnosa Penyakit Obesitas Fauzi Erwis; Devri Suherdi; Ardianto Pranata; Asyahri Hadi Nasyuha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3491

Abstract

Obesity is a disease that is caused by the condition of a person's body that has a lot of fat in his body and is overweight or very fat. This condition is usually caused by the intake of calories into the human body that exceeds the capacity required by the body, where this condition causes a buildup of calories that turn into saturated fat. These conditions cause various chronic diseases such as diabetes, hypertension and coronary heart disease. Therefore, in order to avoid disease conditions caused by obesity, treatment and knowledge are needed so that it is not too late to take action so that the disease does not become severe. One of them uses an expert system, so that people can check for diseases without having to see a doctor. The method used Hybrid Case Base is used to make the best decision from a number of symptoms or alternatives from the diagnosed disease. The aim is to make it easier for the public to find out the diseases they suffer from obesity and be able to check them early. Where this expert system applies the Hybrid Case Base method which has the final result of this research producing an accurate diagnosis with a sample of symptom data used and getting a similarity measurement percentage of 77% with a diagnosis of the possibility of having Type 2 Diabetes.
Co-Authors A, Azanuddin Abdullah Muhazir Afdal Al Hafiz Ahmad Jupri Berutu Aldin Aldin Andre Jackryan Natanaen Sitorus Andri Rinaldy Halim Simangunsong Angga Afrisyah Agatha Ari Romadon Arnold Marpaung Asyahri Hadi Nasyuha Badrul Anwar Dara Phona Dedi Setiawan Devri Suherdi Dicky Nofriansyah Fahrul Rivaldi Hidayat Farid Naufal Alfalah Fauzi Erwis Fifin Sonata Hendryan Winata Heru Prastyo Indah Indriana Ishak Ishak Iskandar Zulkarnain Jaka Prayuda Jaka Prayudha Joan Joan Jufri Halim Khoiril Khomis M. Syaifuddin Masyuni Hutasuhut Milfa Yetri Muhammad Ariq Kautsar Muhammad Iqbal Rangkuti Mukhlis Ramadhan Nurcahyo Budi Nugroho Puji Sari Ramadhan Purwadi Purwadi Rahmat P Dalimunthe Ramasinta Damanik Rejekina Pasaribu Rini Kustini Rizky Ananda Rudi Gunawan Rudi Gunawan Saiful Nurarif Saniman Saniman Siti Julianita Siti Nurmila Rahmatilah Sobirin Sobirin Sri Kusnasari Syahputri, Astri Tria Khairunisa Vivi Anggriani Widiarti Rista Maya Zulfian Azmi Zulkifli Lubis