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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Information Technology and Computer Science (JOINTECS) Zero : Jurnal Sains, Matematika, dan Terapan INFOKUM Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi Syntax: Journal of Software Engineering, Computer Science and Information Technology Instal : Jurnal Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Mathematics and Scientific Computing With Applications Journal of Computer Science and Informatics Engineering Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Abdimas Indonesian Journal DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Ilmiah Teknik Informatika dan Komunikasi Journal Of Informatics And Busisnes Jurnal Ilmiah Research Student
Mhd Ikhsan Rifki
Universitas Islam Negeri Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences

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Journal : Journal of Computer Science and Informatics Engineering

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Comparison of Naïve Bayes and Dempster Shafer Algorithms for the Diagnosis of ARI Diseases Haikal, Baginda Fikri; Hasibuan, Muhammad Siddik; Rifki, Mhd Ikhsan
Journal of Computer Science and Informatics Engineering Vol 4 No 3 (2025): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i3.1161

Abstract

Acute Respiratory Infection (ARI) has a high prevalence in Indonesia, but the manual diagnosis process faces challenges such as limited medical personnel and uncertainty in symptom analysis. This study developed and compared two AI methods, namely Naïve Bayes and Dempster-Shafer, in a web-based expert system to diagnose ARI. Symptom and disease data were collected from literature and experts, then implemented in a PHP and MySQL-based system. Naïve Bayes was used for probability-based classification, while Dempster-Shafer handled uncertainty. Testing was conducted on one case of ARI. Naïve Bayes produced a probability of 21.99% for Pneumonia, while Dempster-Shafer provided a combined probability of 61.6% for five diseases, including Colds, Acute Pharyngitis, and Epiglottitis. The results show that Naïve Bayes is suitable for consistent single diagnoses, while Dempster-Shafer is more appropriate for conditions with overlapping symptoms and uncertain data
Co-Authors Abdul Halim Hasugian Adha Hasibuan, Aidillia Afandi Sahputra Aidil Akbar Simatupang Aidil Halim Lubis Ali Darta Andilala Aulia, M. Arif Bagas F, Muhammad Dwi Prapita Sari Dysa Handira Fadhillah Lubis, Muhammad Arif Fauzi Kurniawan Gibran, M. Khalil Haikal, Baginda Fikri Harjo, William Lutfir Rahman Harun Al Rasyid Hasibuan, Ainun Mardiah Hasibuan, Bunga Lestari Hendra Cipta Ikhwan, Ali Ilka Zufria Inneke Putri Ismail Husein, Ismail Julianti, Miranda Khairul Azis Khalil Gibran, M. Lubis, Nur Azizah M. Khalil Gibran Mhd Furqan Muhamad - Alda Muhamad Alda Muhammad Ezar Raditya Muhammad Ikhsan Muhammad Ikhsan Muhammad Siddik Hasibuan Muhammad, Faisal Nasution, Fadhilah Ramadhani Ong, Russell Pulungan, Miftahul Rizky Rakhmawati, Fibri Rambe, M. Riski Andika Rina Filia Sari, Rina Filia Rini Halila Nasution Riri Syafitri Lubis Salim Salim Siahaan, Ahmad Taufik Al Afkari SIHOMBING, JULIANA SION Suhardi, Suhardi Syamia, Nanda Yusuf Ramadhan Nasution