WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM

Authors

  • Kenny Aldi Universitas Nusa Mandiri
  • Yan Rianto Universitas Nusa Mandiri, Tokai University

DOI:

https://doi.org/10.33480/pilar.v21i2.6414

Keywords:

wastewise, YOLOv8, ESP32-CAM, artificial intelligence

Abstract

The growing volume of global waste poses a significant challenge for effective waste management, particularly in developing countries where awareness and practices around waste sorting remain limited. This study aims to enhance elementary school students' understanding and efficiency in sorting organic and inorganic waste using an interactive, AI-powered educational tool. The proposed system, WasteWise, integrates YOLOv8 for real-time object detection and ESP32-CAM for capturing waste images. A pre-test and post-test experimental design was conducted to assess students’ performance before and after using the system. The results showed a notable improvement in sorting accuracy, increasing from 60% with manual sorting to 90% using the WasteWise system, alongside reduced sorting time. These findings highlight the system's potential not only as an automated waste classification tool but also as a cost-effective and engaging platform for promoting environmental awareness and digital literacy among young learners.

Downloads

Download data is not yet available.

References

Abar, C. A. A. P., Dos Santos Dos Santos, J. M. & Almeida, M. V. de. (2021). Computational Thinking in Basic School in the Age of Artificial Intelligence: Where is the Teacher? Acta Scientiae, 23(6), 270–299. https://doi.org/10.17648/acta.scientiae.6869

Apriyani, R. K., Rustanti, N., Rahayu, D. P. & Hamid, N. D. U. (2023). SOSIALISASI PENGENALAN DAN PEMILAHAN JENIS SAMPAH ORGANIK DAN ANORGANIK DI PANTI ASUHAN ANAK SHALEH. Jurnal Pengabdian Kepada Masyarakat, 2, 43–60. https://jurnal.politeknikpajajaran.ac.id/index.php/pengmas/article/view/46/34

Bawankule, R., Gaikwad, V., Kulkarni, I., Kulkarni, S., Jadhav, A. & Ranjan, N. (2023). Visual Detection of Waste using YOLOv8. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 869–873. https://doi.org/10.1109/ICSCSS57650.2023.10169688

Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W. & Yap, P.-S. (2023). Artificial intelligence for waste management in smart cities: a review. Environmental Chemistry Letters, 21(4), 1959–1989. https://doi.org/10.1007/s10311-023-01604-3

Ghatkamble, R., D, P. B. & Pareek, P. K. (2022). YOLO Network Based Intelligent Municipal Waste Management in Internet of Things. 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), 1–10. https://doi.org/10.1109/ICERECT56837.2022.10060062

Global Waste Management Outlook 2024 - Beyond an age of waste: Turning rubbish into a resource. (2024). United Nations Environment Programme. https://doi.org/10.59117/20.500.11822/44939

Gustiani, D. & Anshori, I. F. (2021). Perancangan Game Edukasi Pengenalan Sampah Organik Dan Anorganik Di Lingkungan TKIT Bustanul’Ulum. 2(2). https://eprosiding.ars.ac.id/index.php/pti/article/view/451/245

Kang, K.-A., Kim, S.-J. & Kang, S. R. (2022). Elementary school students’ awareness of the use of artificial intelligence chatbots in violence prevention education in South Korea: a descriptive study. Child Health Nursing Research, 28(4), 291–298. https://doi.org/10.4094/chnr.2022.28.4.291

Kumar Lilhore, U., Simaiya, S., Dalal, S., Radulescu, M. & Balsalobre-Lorente, D. (2024). Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model. Gondwana Research. https://doi.org/10.1016/j.gr.2024.07.014

Lin, P., Zhao, F., Wang, X. & Chen, Y. (2024). Initiating a novel elementary school artificial intelligence-related image recognition curricula. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19982-3

Lin, W. (2021). YOLO-Green: A Real-Time Classification and Object Detection Model Optimized for Waste Management. 2021 IEEE International Conference on Big Data (Big Data), 51–57. https://doi.org/10.1109/BigData52589.2021.9671821

Natalia Marpaung, D., Iriyanti, Y. & Prayoga, D. (2022). Analisis Faktor Penyebab Perilaku Buang Sampah Sembarangan Pada Masyarakat Desa Kluncing, Banyuwangi (Vol. 13). http://jurnal.fkm.untad.ac.id/index.php/preventif

Sai Susanth, G., Jenila Livingston, L. M. & Agnel Livingston, L. G. X. (2021). Garbage Waste Segregation Using Deep Learning Techniques. IOP Conference Series: Materials Science and Engineering, 1012(1), 012040. https://doi.org/10.1088/1757-899X/1012/1/012040

Sinha, N., Evans, R. F. & Carbo, M. (2023). Hands-on Active Learning Approach to Teach Artificial Intelligence/Machine Learning to Elementary and Middle School Students. 2023 32nd Wireless and Optical Communications Conference (WOCC), 1–6. https://doi.org/10.1109/WOCC58016.2023.10139678

Ultralytics. (2023, November 14). YOLOv8 documentation. Retrieved April 30, 2025, from https://docs.ultralytics.com/models/yolov8/

Urlamma, D., Amani, V., Mounika, G. & Devakumari, K. (2024). Automatic Garbage Classification Using YOLOV8. IARJSET, 11(3). https://doi.org/10.17148/IARJSET.2024.11317

Viswanathan, R. & Telukdarie, A. (2022). The role of 4IR technologies in waste management practices-a bibliographic analysis. Procedia Computer Science, 200, 247–256. https://doi.org/10.1016/j.procs.2022.01.223

Wibysono, A. Y., Susilawati, H. & Matin, I. M. M. (2022). Rancang Bangun Alat Pemilah Sampah Organik dan Non Organik Berbasis Raspberry Pi. Fuse-Teknik Elektro, 2(2), 88. https://doi.org/10.52434/jft.v2i2.2338

Zhao, R., Zeng, Q., Zhan, L. & Chen, D. (2024). Multi-target detection of waste composition in complex environments based on an improved YOLOX-S model. Waste Management, 190, 398–408. https://doi.org/10.1016/j.wasman.2024.10.005

Downloads

Published

2025-09-23

How to Cite

Aldi, K. ., & Rianto, Y. (2025). WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM. Jurnal Pilar Nusa Mandiri, 21(2), 171–177. https://doi.org/10.33480/pilar.v21i2.6414

Issue

Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2025

Section

Articles

License

Copyright (c) 2025 Kenny Aldi, Yan Rianto

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:

  1. Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  2. Author is 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 the acknowledgement of its initial publication in this journal.
  3. Author is permitted and encouraged to post his/her 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 the published work (See The Effect of Open Access).
    Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.