Implementation of Expert System Applications using Forward Chaining to Detect Dental Health - Zahra Zahra; Melyani Melyani; Faif Yusuf; Metty Titin Herawati; Suci Royanti; Athiy Dina Rosihana (University of Science and Computer Technology, 2025)
Dental diseases remain one of the most common health issues globally, often resulting from a lack of early detection and limited access to dental specialists. This research presents the implementation of an expert system that uses forward chaining to diagnose dental health conditions based on user-reported symptoms. The system integrates a knowledge base modeled from expert consultation with dentists, consisting of symptom sets and rule-based logic. Findings indicate that the Forward Chaining approach is effective for step-by-step rule evaluation and generates accurate diagnoses of diseases such as caries, gingivitis, periodontitis, halitosis, and pulpitis. The study demonstrates that expert systems can support preliminary dental screening and improve public awareness of dental health.