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All Journal INFORMAL: Informatics Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Antivirus : Jurnal Ilmiah Teknik Informatika Among : Jurnal Pengabdian Masyarakat Journal of Information System and Technology (JOINT) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Abdimas Awang Long: Jurnal Pengabdian dan Pemberdayaan Masyarakat JUSTIFY : Jurnal Sistem Informasi Ibrahimy J-ENSITEC (Journal of Engineering and Sustainable Technology) Jurnal Aplikasi Sistem Informasi dan Elektronika Journal Of Humanities Community Empowerment ABDIMASTEK Computing and Information System Journal
Wijaya, Guruh
Universitas Muhammadiyah Jember
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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EARLY DETECTION OF DIABETES MELLITUS TYPE 1, TYPE 2, AND GESTATIONAL DIABETES MELLITUS (GDM) USING WEBSITE-BASED CERTAINTY FACTOR Rayhan Ibrahim; Guruh Wijaya; Dudi Irawan
Computing and Information System Journal Vol. 2 No. 1 (2026): Inovasi Teknologi Cerdas Berbasis AI, IoT, dan Data Mining di Era Digital
Publisher : IndoCompt Publisher

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Abstract

The development of information technology plays an important role in supporting healthcare services, particularly in assisting the early detection of diseases. One chronic disease that requires early detection is Diabetes Mellitus, which consists of Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, and Gestational Diabetes Mellitus (GDM). These three types of diabetes have similar early symptoms, making them difficult to recognize by the general public. Therefore, this study aims to design and develop a web based expert system to detect Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, and Gestational Diabetes Mellitus (GDM) using the Certainty Factor method. This method is used to calculate the level of diagnostic confidence based on symptoms selected by users, referring to confidence weights provided by medical experts. The problem-solving process is carried out through Certainty Factor calculations using a weighted average approach to generate the percentage of diagnostic confidence. Based on testing and validation results through manual calculations and expert evaluation, the developed system achieved an accuracy rate of 87.10%. The results indicate that this expert system can be used as a supporting tool for the early detection of Diabetes Mellitus prior to further medical examination.
Co-Authors Ari Eko Wardoyo Ari Eko Wardoyo Dodik Aris Setiawan Fitriyah, Nur Qodariyah Ginanjar Abdurrahman Habibatul Azizah Al-Faruq Hepi Wahyuningsih Irawan, Dudi Jenri Putra Pratama Lintang Setyo Kurniawati Muhammad Firdausi Noeralita Vefiliana Nur Qodariyah Fitriyah Oktavianto, Hardian Rahman, Miftahur Rayhan Ibrahim Resti Sutianingtyas Rojabi, Ilzam Saifudin, Ilham Sri Kusumadewi Sulistyo, Henny Wahyu Taufiq Timur Warisaji Ulya Anisatur Rosyidah Warisaji, Taufiq Timur Yusril Izzi Arlisa Amiri Zainul Arifin Zainul Arifin ZAINUL ARIFIN