Penggunaan Metode Analisis Data Untuk Rekomendasi Menu Makanan Berdasarkan Persediaan Bahan dan Preferensi Pengguna

Abstract

Cooking represents a significant and enjoyable activity, enabling individuals to create delectable dishes. However, the challenge of discovering recipes that align with available ingredients and personal preferences persists. To tackle this issue, a recommendation system is introduced, employing a content-based filtering approach that utilizes Jaccard similarity to harness users' ingredient ownership. Additionally, an item-based collaborative filtering approach employing cosine similarity is utilized to propose recipes based on user preferences and those of fellow users. This comprehensive approach delivers precise and pertinent recipe recommendations, optimizing ingredient utilization and elevating the cooking experience. Furthermore, the assessment of recommendations based on user preferences reveals varying outcomes.