This study developed a Certainty Factor (CF)–based expert system to support nutrition consultation and stunting prevention in coastal settings using Bengkalis Regency as a case study. A design science approach was employed to analyze local service constra
Abstract
This study developed a Certainty Factor (CF)–based expert system to support nutrition consultation and stunting prevention in coastal settings using Bengkalis Regency as a case study. A design science approach was employed to analyze local service constraints, acquire expert knowledge, formalize a rule base, implement an offline-first prototype (mobile client and admin dashboard), and evaluate its performance. Knowledge was elicited from nutritionists, midwives, and community health workers and encoded as IF–THEN rules with expert confidence weights; evidence (anthropometry, infection history, infant and young child feeding, sanitation, and socioeconomic factors) was mapped to CF values and combined to yield risk scores and categories with explainable rule traces. Functional testing showed all user-story scenarios passed as expected, while initial expert validation and usability checks indicated the prototype provided rapid and standardized assessments suitable for first-line services. The results suggest the CF approach is feasible for coastal contexts with limited connectivity and can accelerate early screening and referrals. Future work will expand the local knowledge base, integrate electronic records, and conduct wider field trials to measure effectiveness at scale
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