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All Journal Journal of Computer Science and Informatics Engineering Jurnal Media Teknik Elektro dan Komputer
Haikal, Baginda Fikri
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Computer Science & IT Electrical & Electronics 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
Sistem Pakar Diagnosis Penyakit Pernapasan Menggunakan Metode Forward Chaining Haikal, Baginda Fikri; Siregar, Reza Abdillah
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 1 (2025): Metrokom : Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i1.97

Abstract

Respiratory diseases are one of the health disorders whose prevalence continues to increase and require rapid and accurate diagnosis to support effective medical treatment. This study aims to develop an expert system for diagnosing respiratory diseases using the forward chaining method. This method was chosen for its ability to perform data-driven reasoning, starting from the facts of the symptoms experienced by the patient, which then trigger certain rules to produce a diagnostic conclusion. The system is designed with a knowledge base containing validated data on symptoms and types of respiratory diseases, as well as an inference engine to process diagnostic rules. The system is implemented using a web-based programming language with database integration that stores information on symptoms, diseases, and reasoning rules. Test results show that the system is capable of providing quick and accurate initial diagnoses based on the symptom data entered, and can serve as a tool for medical professionals and the public in detecting respiratory diseases early. This research is expected to contribute to the development of artificial intelligence-based health technology that supports more effective and efficient medical services.
Co-Authors Mhd Ikhsan Rifki Muhammad Siddik Hasibuan Siregar, Reza Abdillah