Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)

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Smart Trash Box Technology of Computer Vision to Support Ecogreen Campus

Authors
Husnawati Husnawati1, Ahmad Bahri Joni Malyan1, Rian Rahmanda Putra1, *, Suzan Agustri2
1Computer Engineering, Sriwijaya State Polytechnic, Palembang, Indonesia
2Information System, Universitas Indo Global Mandiri, Palembang, Indonesia
*Corresponding author. Email: rianrahmanda@polsri.ac.id
Corresponding Author
Rian Rahmanda Putra
Available Online 27 February 2024.
DOI
10.2991/978-94-6463-386-3_25How to use a DOI?
Keywords
Eco-green; Computer Vision; CNN; Deep Learning; Image Processing
Abstract

Waste from daily human activities which is one of the causes of natural disasters such as floods. Research on the environment, especially on technology and environmental waste management systems, is still ongoing. Waste management includes organic waste and non-organic waste. The problem that occurs in the campus environment is the large amount of rubbish scattered around, much of this rubbish comes from used goods, and comes from human activities in daily life. Waste management on Campus is not yet well organized. In this research, technology will be developed for Smart Trash Boxes or an intelligent system for trash boxes that can classify types of organic and non-organic waste in the campus environment by applying Computer Vision techniques in image processing as an effort to support the eco-green campus. In its application, the deep learning method with the CNN algorithm is used, so that the classification results obtained in this research can differentiate between organic waste and non-organic waste to be utilized or recycled and can preserve natural resources and the environment. Finally, the use of the CNN model does not require big data for training research to obtain high accuracy. This research produces a validated accuracy value of 0.8034.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
Series
Advances in Engineering Research
Publication Date
27 February 2024
ISBN
978-94-6463-386-3
ISSN
2352-5401
DOI
10.2991/978-94-6463-386-3_25How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Husnawati Husnawati
AU  - Ahmad Bahri Joni Malyan
AU  - Rian Rahmanda Putra
AU  - Suzan Agustri
PY  - 2024
DA  - 2024/02/27
TI  - Smart Trash Box Technology of Computer Vision to Support Ecogreen Campus
BT  - Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
PB  - Atlantis Press
SP  - 220
EP  - 229
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-386-3_25
DO  - 10.2991/978-94-6463-386-3_25
ID  - Husnawati2024
ER  -