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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Hilirisasi IPTEKS (JHI) Community Engagement and Emergence Journal (CEEJ) JURSIMA (Jurnal Sistem Informasi dan Manajemen) AMAR (Andalas Management Review) Ekasakti Jurnal Penelitian dan Pengabdian (EJPP) JEBI (Jurnal Ekonomi dan Bisnis Islam) Jurnal Publikasi Teknik Informatika (JUPTI) JURSIMA Jurnal Pengabdian kepada Masyarakat Dewantara Jurnal Sistem Informasi dan Manajemen
Putra, Sapta Eka
Universitas Tamansiswa Padang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Library & Information Science Public Health Social Sciences Veterinary Other

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Journal : JITK (Jurnal Ilmu Pengetahuan dan Komputer)

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EXPERT SYSTEM FOR DISEASE IDENTIFICATION BASED ON HEMATOCHEZIA SYMPTOMS WITH NAÏVE BAYES METHOD Dasril Aldo; Alwendi Alwendi; Adanti Wido Paramadini; Ilwan Syafrinal; Sapta Eka Putra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 9 No 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5064

Abstract

Hematochezia is a common clinical symptom in various gastrointestinal diseases, requiring accurate diagnosis for effective treatment. This study aims to develop an expert system for the rapid and precise identification of hematochezia-causing diseases. The expert system is designed to assist patients in efficiently recognizing diseases, minimizing treatment failure risks. It employs the Naïve Bayes method, a data calculation approach involving summing combinations and frequencies of each dataset. The expert system methodology begins with training using a dataset comprising hematochezia symptoms and corresponding disease diagnoses. The dataset is input into a database as training data. Subsequently, it undergoes classification and training stages. Symptom data can then be processed using the Naïve Bayes method. The system's end result displays probability values for each disease based on provided symptoms. This analysis relies on specific symptoms selected by the user, such as Rectal Pain, Hematochezia, Constipation, Fatigue, and Abdominal Cramps. It yields a Hemorrhoids diagnosis with a posterior probability of 0.514738. In testing with 35 sample cases, the expert system exhibited a remarkable accuracy rate of 94.29%. This expert system efficiently supports disease diagnosis based on hematochezia symptoms, aiding in swift and accurate identification.
Co-Authors Adanti Wido Paramadini Berri Brilliant Albar Dasril Aldo Dedi Rahman Habibie Febriani Febriani Febriani Febriani Ilwan Syafrinal Indah Maya Sari Lenny Hasan Masriadi Masriadi Masriadi, Masriadi Mayang Larasati Mayang Larasati Munir, Zainul Rahmat Rahmat Sherly Yulinda Trio Candra Yo Widya Rahmawati Yuliarni Putri Yuni Candra Zainul Munir