<![CDATA[Household food waste remains pervasive, driven by suboptimal meal planning and the underuse of available ingredients, with particularly acute impacts in Indonesia. This study presents a mobile application using YOLOv12n for multi-ingredient detection that translates recognized items into actionable recipes, and it evaluates user acceptance through the Technology Acceptance Model. On this implementation, the detection module attains mAP at 0.5 of 0.579 and mAP from 0.5 to 0.95 of 0.331. This study implements a Flutter application with YOLOv12n multi-ingredient detection integrated with TheMealDB and observes 219 users. The instrument validity is established and reliability is strong. While, Structural Equation Modelling supports 3 hypotheses, meanwhile Perceived Usefulness to Behavior Intention is not significant, indicating an indirect pathway via attitude. In conclusion, the solution is feasible for daily use, and strengthening perceived usefulness and ease of use appears to be a promising route to increase adoption and help reduce household waste.]]>
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