Real-Time Waste Detection System Using YOLOv12 with Transfer Learning
DOI:
https://doi.org/10.30871/jaic.v10i1.12050Keywords:
YOLOv12, Waste Detection, Transfer Learning, Computer Vision, Web-Based SystemAbstract
Waste sorting at the source remains a major challenge in Indonesia due to limited public awareness and the absence of accessible tools for waste classification. While YOLO-based object detection has been widely applied for waste detection, the adoption of the latest YOLO architecture in web-based, real-time public-oriented systems remains limited. This study aims to develop and experimentally evaluate a web-based waste detection system using YOLOv12 with a transfer learning approach to classify waste into organic, inorganic, and hazardous (B3) categories along with their subcategories. The system was developed using the Flask framework and supports image upload and real-time camera-based detection. A real-world dataset was annotated and divided into training, validation, and testing sets for experimental evaluation. The proposed model achieved a precision of 0.86, recall of 0.74, [email protected] of 0.83, and [email protected]:0.95 of 0.68, with an average inference time of 0.0187 seconds per image (53.40 FPS). Overall, these results indicate that YOLOv12 with transfer learning provides an effective balance between accuracy and inference speed for web-based real-time waste detection systems, supporting its applicability for practical waste sorting solutions.
Downloads
References
[1] Kementerian Lingkungan Hidup dan Kehutanan, “Capaian Kinerja Pengelolaan Sampah.” Accessed: Jun. 20, 2025. [Online]. Available: https://sipsn.kemenlh.go.id/sipsn/
[2] E. Supriyanto, R. R. Isnanto, and S. H. Purnomo, Deteksi Dan Klasifikasi Kesehatan Ayam Menggunakan Yolo Dengan Google Colab. Pagar Alam: PENERBIT LD Media, 2025.
[3] O. Soerya, N. Utomo, F. Utaminingrum, and E. R. Widasari, “Implementasi YOLO versi 3 untuk Mengidentifikasi dan Mengklasifikasi Sampah Kantor berbasis NVIDIA Jetson Nano,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 6, pp. 2829–2834, Jun. 2022, [Online]. Available: http://j-ptiik.ub.ac.id
[4] H. Abdillah, A. N. Syahbana, G. I. Al Husain, and S. Agustin, “Detektif Sampah : Klasifikasi Jenis Sampah Organik dan Anorganik Menggunakan Metode YOLOv5 Berbasis Website,” Jurnal INOVATIF WIRA WACANA, vol. 3, no. 2, pp. 128–135, Aug. 2024.
[5] N. Suwela and M. Z. Hedriyadi, “Deteksi Jenis Sampah Menggunakan Metode Transfer Learning YOLO-V8,” Jurnal Sistem Informasi dan Sains Teknologi, vol. 7, no. 1, Feb. 2025.
[6] A. A. Mustapha, S. ’Atifah Saruchi, M. I. Solihin, F. K. Aldeen, and A. A. M. Al-Talib, “Exploring the Performance of YOLOv11: Detecting Compostable and Non-Compostable Kitchen Waste in Real-Time Applications,” The 2025 International Conference on Artificial Life and Robotics (ICAROB2025), pp. 13–16, Feb. 2025.
[7] M. H. Dipo et al., “Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model,” Digital, vol. 5, no. 2, pp. 1–17, Jun. 2025, doi: 10.3390/digital5020019.
[8] Keylabs, “YOLOv8 vs Faster R-CNN: A Comparative Analysis,” Keylabs. Accessed: Aug. 17, 2025. [Online]. Available: https://keylabs.ai/blog/yolov8-vs-faster-r-cnn-a-comparative-analysis/?utm_source=chatgpt.com
[9] S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” J Big Data, vol. 8, May 2021, doi: 10.1186/s40537-021-00434-w.
[10] Ultralytics Inc., “YOLO12: Attention-Centric Object Detection.” Accessed: Jun. 21, 2025. [Online]. Available: https://docs.ultralytics.com/models/yolo12/
[11] A. E. Putra, M. F. Naufal, and V. R. Prasetyo, “Klasifikasi Jenis Rempah Menggunakan Convolutional Neural Network dan Transfer Learning,” JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 9, no. 1, Apr. 2023.
[12] M. W. S. Sanjaya, Deep Learning Citra Medis Berbasis Pemrograman Python. Bandung: BOLABOT, 2023.
[13] I. G. N. Suryantara, Michael, J. F. Andry, and J. A. Ginting, “PENGEMBANGAN APLIKASI OPERASIONAL RESTORAN DENGAN FRAMEWORK SCRUM (STUDI KASUS: RESTORAN PT.XYZ),” Infotech: Journal of Technology Information, vol. 9, no. 2, pp. 117–128, Nov. 2023, doi: 10.37365/jti.v9i2.168.
[14] L. Syaputri, E. G. Putra, E. Syahrani, E. Dwian, and F. Purwani, “PERBANDINGAN EFEKTIVITAS METODE WATERFALL DAN AGILE DALAM PENGEMBANGAN SISTEM INFORMASI SEBUAH SYSTEMATIC LITERATURE REVIEW,” Journal of Scientech Research and Development, vol. 6, no. 2, pp. 262–273, 2024, doi: 10.35870/jtik.v6i1.380.
[15] S. Iqbal, I. Al-Azzoni, G. Allen, and H. U. Khan, “Extending UML Use Case Diagrams to Represent Non-Interactive Functional Requirements,” E-Informatica Software Engineering Journal, vol. 14, no. 1, pp. 97–115, 2020, doi: 10.37190/e-INF200104.
[16] S. Nabila, A. R. Putri, A. Hafizhah, F. H. Rahmah, and R. Muslikhah, “Pemodelan Diagram UML Pada Perancangan Sistem Aplikasi Konsultasi Hewan Peliharaan Berbasis Android (Studi Kasus: Alopet),” Jurnal Ilmu Komputer dan Bisnis, vol. 12, no. 2, pp. 130–139, Nov. 2021, doi: 10.47927/jikb.v12i2.150.
[17] J. F. Andry, F. S. Lee, K. Christianto, Y. Purnomo, A. Chakir, and L. Liliana, “User-Centric Development of Good Delivery Applications Using Design Thinking and Business Model Canvas,” EAI Endorsed Transactions on Scalable Information Systems, vol. 12, no. 4, Oct. 2025, doi: 10.4108/eetsis.8223.
[18] R. S. Samosir, E. Lumba, and P. P. Situmorang, “A PROTOTYPE OF DIGITAL LIBRARY APPLICATION USING MICROFRAMEWORK FLASK,” Jurnal Techno Nusa Mandiri, vol. 19, no. 2, pp. 96–103, Sep. 2022, doi: 10.33480/techno.v19i2.3006.
[19] N. M. D. Febriyanti, A. A. K. O. Sudana, and I. N. Piarsa, “Implementasi Black Box Testing pada Sistem Informasi Manajemen Dosen,” Jurnal Ilmiah Teknologi dan Komputer, vol. 2, no. 3, Dec. 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Adellia Jovina, Ester Lumba
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
