Transfer Learning Approaches for Non-Organic Waste Classification: Experiments Using MobileNet and VGG-16 - Sari, Zamah; Basuki, Setio (Universitas Muhammadiyah Malang, 2025)
This paper develops machine learning (ML) models for classifying non-organic waste automatically. The goal is to support more effective waste management by increasing recycling rates, reducing landfill use, and minimizing environmental impact. The ML models proposed in this paper classify 20 types of non-organic waste collected from the internet, which consists of 2,552 instances. Our experiments reveal several key findings. First, MobileNet, which achieved 86% accuracy, outperforms VGG-16, which reaches only 72% accuracy. Second, both models show good classification performances in classifying glass bottles, toothbrushes, and cigarette butts. Third, both models suffer from misclassification in visually similar categories, especially when it comes to paper-based waste like books, cardboard, foam packaging, and carton packaging. Fourth, MobileNet has difficulty detecting plastic packaging, carton packaging, and books, while VGG-16 exhibits higher misclassification rates for foam packaging, cardboard, and newspapers. These results pose a further critical development of the model to classify non-organic waste with similar textures and shapes. Moreover, it presents the urgency of improving the model to distinguish visually similar waste materials. Considering the number of labels used in this paper compared with existing studies, the findings demonstrate the competitiveness of our models for non-organic waste classification.