Implementasi Forward Chaining dan Certainty Factor pada Sistem Pakar Diagnosis Penyakit Kulit Berbasis Android
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1298Keywords:
Expert System; Forward Chaining; Certainty Factor; Skin Disease; AndroidAbstract
Skin diseases are common health problems in the community and frequently present overlapping symptoms, making independent early identification difficult. Limited access to dermatologists encourages self-medication without adequate information, which may worsen the condition. This study applies the Forward Chaining and Certainty Factor methods to SkinCheck, an Android-based expert system application for the early diagnosis of skin diseases. Forward Chaining serves as a data-driven inference engine to determine disease candidates from the symptom facts selected by the user, while Certainty Factor measures the confidence level of the diagnosis by combining expert confidence weights using the CF Combine formula. The system's knowledge base consists of 20 skin diseases, 52 symptoms, and 59 diagnostic rules validated by a medical professional, complemented by a diagnosis history feature and automatically generated health education articles based on a Large Language Model. The agreement between manual calculations and the application's output indicates that the inference process and confidence level calculations operate consistently with the algorithm design. Functional testing using Black Box Testing indicates that all main features operate validly, while System Usability Scale testing involving 30 respondents obtained an average score of 71.67, falling within the Acceptable category with Grade Scale C. The main contribution of this study lies in applying the combined Forward Chaining and Certainty Factor methods on the Android platform covering 20 skin diseases to provide an early diagnosis accompanied by a measurable confidence level, developing a knowledge base validated by medical professionals, and integrating a diagnosis history feature alongside automated health education articles powered by a Large Language Model (LLM) to enhance user health literacy.
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