Imam Husni, Al Amin and Dwiki Vio, Setyadarma and Setyawan, Wibisono Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application. Revue d'Intelligence Artificielle.
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Abstract
The surge in global population and economic activity has precipitated a significant escalation in waste generation, with projections indicating potential daily global waste levels of 11 million tons by the century's conclusion. This intensification presents a formidable challenge requiring innovative solutions, one of which is the utilization of machine learning algorithms for automated waste categorization. This study explores the integration of the Faster R-CNN algorithm within an Android application, aimed at streamlining waste management through the identification and categorization of recyclables. The proliferation of smartphone usage—specifically Android applications— provides an accessible platform for mass education on waste management, thereby contributing to potential waste reduction. This study deployed a visual testing approach to evaluate the application's performance across diverse waste categories, including cardboard, glass, plastic, and paper. During each testing session, images of each waste type were captured and the objects were methodically rotated by approximately 20 degrees, enhancing the robustness of the machine learning model. The implementation of the Faster R-CNN algorithm within an Android environment, as exemplified in this study, has demonstrated noteworthy potential to revolutionize waste management. An impressive accuracy rate of 98.106% was achieved in the detection and classification of various waste types. Such a technological innovation can augment the efficiency of waste categorization and recycling efforts. Thus, the study contributes to the development of a more sustainable and environmentally responsible waste management system.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Faculty / Institution: | Fakultas Teknologi Informasi |
| Depositing User: | Fakultas Ekonomi |
| Date Deposited: | 13 Mar 2024 02:24 |
| Last Modified: | 13 Mar 2024 02:24 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/9949 |
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