Combination Forward Chaining and Certainty Factor Methods for Selecting the Best Herbs to Support Independent Health

The use of herbal medicine as an alternative treatment is increasingly popular due to its natural benefits and cultural significance. However, a lack of public knowledge about the effectiveness, appropriate dosages, and processing methods of herbal remedies poses a significant barrier to their proper utilization. This knowledge gap often leads to suboptimal or even unsafe usage of herbal medicines. To address this issue, this study proposes an application-based system combining the Forward Chaining and Certainty Factor methods to provide personalized recommendations for the best herbal remedies supporting self-health management. The research aims to enhance accessibility to reliable information on herbal treatments while ensuring accurate and user-specific recommendations. By utilizing the ForwardChaining and Certainty Factor method, this system identifies suitable herbal plants based on the type of disease, processing techniques, recommended dosages, and duration of treatment. Meanwhile, the Certainty Factor method calculates the level of certainty for each recommendation provided. The study’s results showed a validation rate of 90%, indicating that the combination of these two methods effectively bridges the gap between traditional herbal knowledge and modern health needs. This study concludes that the system offers a practical tool for individuals to select and use herbal treatments safely and effectively, promoting better health outcomes.