A Hybrid Recommendation System for Pregnancy-Safe Skincare: Integrating Keyword-Based and Rule-Based Classification with Content-Based Filtering - Putri, Jihan Syahira Adnanda; Haryono, Kholid (Informatika, Universitas Jenderal Soedirman, 2025)
Pregnant women struggle to select safe skincare products, often relying on social media and blog searches, and manual ingredient checking. Choosing safe ingredients is essential, as exposure to unsafe substances may lead to teratogenic effects and endocrine disruption, which can result in fetal abnormalities such as retinoic acid embryopathy and neurodevelopmental disorders. Exposure to retinoids, for instance, has been associated with a 20–30% incidence of fetal retinoid syndrome in affected pregnancies. This study develops an integrated recommendation system using three techniques: (1) keyword-based classification with regular expressions to detect 50 unsafe ingredients across 8 categories; (2) rule-based classification using IF-THEN statements matching products with 5 pregnancy-related skin conditions; and (3) content-based filtering utilizing TF-IDF vectorization and cosine similarity for safer alternatives. The system achieved 86.25% accuracy in safety classification, with high recall (97.50%) indicating strong ability to identify safe products. However, moderate precision (79.59%) suggests some unsafe products were misclassified as safe, highlighting need for improvement in safety-critical contexts. Pilot user evaluation using ResQue framework with 10 participants yielded scores of 4.50–4.85 across 8 dimensions, achieving 4.65 overall average. This research demonstrates effective integration of multiple recommendation methods in context-sensitive applications, enabling safer product selection during pregnancy. By providing accessible, personalized, and evidence-based information, the system enables pregnant women to make informed skincare decisions and continue their routines despite limited access to healthcare services.