Implementing Mobile-based AI in Household Waste Type and Condition Classification - Suwarno Suwarno; Joen Lie; Mangapul Siahaan (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
Urbanization and population growth have significantly increased waste generation, creating challenges for effective waste management and recycling. Improper waste sorting and management often results to unrecyclable waste contaminating recycling streams or recyclable waste ending up in landfill. This research presents a mobile-based waste classification application that integrates YOLOv11n for real-time object detection, and uses TensorFlow Lite with a Flutter-based user interface. The model was trained on a dataset of 4,410 images, which combines self-gathered images and images from Kaggle dataset. The images are then augmented to 10,936 images covering 23 waste classes, including organic, inorganic, hazardous, and residual types, with their recyclability conditions. The application allows users to detect objects using their phone camera, to identify their classification and condition, as well as receive actionable 3R (Reduce, Reuse, Recycle) recommendations. Evaluation results show a precision of 0.5963, recall of 0.60563, mAP@0.5 of 0.62246, and mAP@0.5:0.95 of 0.5279, indicating decent classification despite challenges posed by visually similar objects and variable backgrounds. Overall, the system demonstrates the feasibility of deploying a lightweight AI model on mobile devices in hopes of supporting proper waste segregation, increase user awareness, and potentially reduce contamination in recycling streams through practical waste classification.