Enhancing Waste Classification with MobileNetV2: Adding a Plastic Sachets Class for Sustainable Management - Pritama, Argiyan Dwi; Sandy Kusuma, Velizha; Baihaqi, Wiga Maulana; Subarkah, Pungkas (Edu Komputika Journal, 2025)
The issue of waste management remains a critical concern due to its adverse impact on the environment. This research enhances a deep learning-based waste classification model by introducing a new class, namely plastic sachets, to broaden the classification scope and increase the model's relevance to waste types commonly found in the community. The dataset used is an extended version of a previous open-source dataset, comprising 2,968 images divided into seven classes. Data preprocessing steps include stratified data splitting, data augmentation to increase image diversity, and pixel normalization. The model adopts the MobileNetV2 architecture through a transfer learning approach, utilizing 2D Global Average Pooling and Dense layers with softmax activation for multi-class classification. Evaluation using precision, recall, and F1-score demonstrated strong performance, with an overall accuracy of 97%. While the model performs well across most classes, further improvement is needed for minority classes such as plastic sachets. This study highlights the promising potential of deep learning in supporting automated waste sorting to promote sustainable waste management practices in Indonesia.