Automated Acne Type Identification Through Forward Chaining Approach

Aris Rakhmadi, Naura Fikamelyalla, Sri Winiarti, Esi Putri Silmina, Umi Fadlillah, Yusuf Sulistyo Nugroho

Abstract


Acne, a prevalent dermatological condition, poses significant physical and psychological challenges. Despite its widespread impact, timely and accessible diagnosis remained a barrier for many, emphasizing the need for innovative solutions. This study introduced an online consultation system for acne-type identification, leveraging a forward chaining approach within an AI-powered expert system. The system analyzed user-reported symptoms—such as severity, location, and appearance—using a rule-based inference mechanism to provide accurate diagnoses and tailored treatment recommendations. Developed using a prototype model, the system’s knowledge base was enriched through observations, literature reviews, and expert interviews, ensuring reliability and clinical relevance. Iterative testing, including black-box evaluations and a System Usability Scale (SUS) assessment, confirmed the system's functionality and user satisfaction, with a SUS score of 86.5, indicating high acceptance. The system bridged critical gaps in dermatological care, particularly for underserved communities, by enabling rapid, user-centric diagnostics and personalized recommendations. The research underscored the transformative potential of artificial intelligence and expert systems in healthcare. By integrating accessibility, scalability, and precision, the proposed system addressed the challenges of acne management and set a foundation for future advancements in dermatological diagnostics.

Keywords


Acne Type Identification, Forward Chaining, Expert System, Healthcare, Artificial Intelligence

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DOI: http://dx.doi.org/10.21927/ijubi.v8i1.5377

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Alma Ata University
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