<![CDATA[Apis cerana honey bee cultivation in Indonesia faces a major constraint of limited access to expert apiculturists for fast and accurate colony condition diagnosis. The manual and subjective diagnostic process creates uncertainty because one symptom can indicate multiple possible conditions. This research aims to develop a web-based expert system using the Certainty Factor (CF) method to diagnose Apis cerana honey bee colony conditions. The research methodology follows the Expert System Development Life Cycle (ESDLC) consisting of four stages: intelligence, design, selection, and implementation. The resulting knowledge base consists of 15 symptoms, 5 colony conditions, 5 IF-THEN rules, and a CF value matrix. The results show that the developed expert system achieved 100% accuracy when tested on 20 colony samples compared to expert diagnosis. All system functions performed according to specifications based on black box testing of nine scenarios. The system displays diagnostic ranking with confidence levels in percentages and provides a CF Combine calculation detail feature for diagnostic process transparency. Expert validation results state that the system is feasible for use as a diagnostic tool. This system provides a practical solution for farmers in various locations to diagnose colony conditions independently, quickly, and in a standardized manner.]]>
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