IMPLEMENTATION OF FORWARD CHAINING AND NAÏVE BAYES TO DETERMINE THE SEVERITY OF MEASLES IN TODDLERS - Andi Husnul Khatimah (LPPM Universitas Abdurrab, 2026)
Measles is a contagious disease that often affects toddlers and can cause serious complications if not treated appropriately. This study aims to implement the Forward Chaining and Naïve Bayes methods to determine the severity of measles in toddlers. This study does not develop a software-based system, but rather focuses on conceptual implementation and manual calculations using decision tables, rule bases, and probability calculations. The research data were obtained from 20 patients who underwent discussion and validation with experts at Lanto Dg Pasewang Regional General Hospital. A total of 16 symptoms were used as research variables in the analysis process. The Forward Chaining method was applied to determine the diagnosis based on rules designed in accordance with expert knowledge, while the Naïve Bayes method was used to calculate statistical classification probabilities based on available case data.The results showed that both methods were able to effectively determine the severity of measles. However, the Naïve Bayes method produced a higher level of accuracy, while the Forward Chaining method had a lower accuracy rate. The accuracy percentages obtained were 75% for Forward Chaining and 85% for Naïve Bayes. Thus, the probabilistic-based approach provides more optimal determination results than the rule-based approach in the context of this study.