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All Journal ComEngApp : Computer Engineering and Applications Journal Journal of Information Systems and Informatics Jurnal Generic REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Mandiri IT DEVOTE: Jurnal Pengabdian Masyarakat Global The Indonesian Journal of Computer Science
Ricy Firnando
University of Sriwijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences Other

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Journal : Jurnal Mandiri IT

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Performance analysis of MobileNetV2 based automatic waste classification using transfer learning Firnando, Ricy; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi; Willy; Abdurahman; Isnanto, Rahmat Fadli
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.451

Abstract

The significant increase in global waste requires innovative and accessible solutions, which aligns with Sustainable Development Goal (SDG) 12, which focuses on reducing the environmental impact of human activities. Automatic waste sorting using Computer Vision and Deep Learning offers a promising alternative to labor-intensive and risky manual methods. This study presents the design, implementation, and comprehensive performance analysis of an automated waste classification system, with a specific focus on evaluating its feasibility on hardware without specialized GPU accelerators. By leveraging transfer learning on a lightweight Convolutional Neural Network (CNN) architecture, MobileNetV2, a model was trained to classify six common waste categories: cardboard, glass, metal, paper, plastic, and other waste. The public “Garbage Classification” dataset from Kaggle, consisting of 2,527 images, was used as the basis for training and validation. The experiment was conducted using the tensorflow-cpu library, which does not require a dedicated GPU accelerator. After 10 training epochs, the model achieved a significant validation accuracy of 86.73%. Computational performance analysis showed an efficient average training time of 31.17 seconds per epoch and a fast average inference time of 14.47 milliseconds per image (~69 FPS) on the validation dataset. These findings demonstrate the feasibility of developing an effective AI-based waste classification system on hardware without a GPU accelerator, providing a realistic performance benchmark for the development of low-cost smart bins with embedded waste sorting solutions in the future, thereby contributing to sustainable waste management practices.
Co-Authors Abdurahman Aditya Putra Perdana Prasetyo Ahmad Fali Oklilas Ali Ibrahim Allsela Meiriza, Allsela Alvico, Alvico Ariani, Ardina B. Azhar, Iman Saladin Buchari, M. Ali Buchari, Muhammad Ali Dudifa, Aldi Dwi Rosa Indah Febriady, Mukhlis Firdaus Gumay, Naretha Kawadha Pasemah Hasbiallah, Muhammad Jidan Kawadha Pasemah Gumay, Naretha Kemahyanto Exaudi Marjusalinah, Anna Dwi Muhammad Naufal Rachmatullah Pacu Putra Rahmat Fadli Isnanto Riyuda, Rafki Sahasika RR. Ella Evrita Hestiandari Sarmayanta Sembiring Seprina, Iin Siti Nurmaini Sukemi Syahbani, Muhammad Husni Vindriani, Marsella Willy Willy, Willy Winda Kurnia Sari Yadi Utama