Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products

Jaka Fajar Fatriansyah; Aurelia Divanti Thaqia; Muhammad Syaikh Rohman; Fernanda Hartoyo; Donanta Dhaneswara

doi:10.1063/5.0178584

Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products

Jaka Fajar Fatriansyah, Aurelia Divanti Thaqia, Muhammad Syaikh Rohman, Fernanda Hartoyo, Donanta Dhaneswara

Department of Metallurgical and Material Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Plastic waste is one of the problems that must be considered because it can negatively impact the environment. These problems can be above using the Life Cycle Assessment (LCA) approach to increase awareness regarding the environmental impact caused by plastic products. However, the LCA method is quite complex. So as to facilitate the work of users can be done by implementing LCA on plastic packaging products using a user-friendly system. In this study, the creation of a deep learning model algorithm to predict the LCA score of plastic bottle packaging products. The results obtained are that deep learning programs can be used in predicting LCA scores, even if there is a difference between the actual and predicted values.

Original language	English
Title of host publication	AIP Conference Proceedings
Editors	H. Shankar, P. Thangaraj, K. Mohana Sundaram
Publisher	American Institute of Physics Inc.
Edition	1
ISBN (Electronic)	9780735447653
DOIs	https://doi.org/10.1063/5.0178584
Publication status	Published - 15 Dec 2023
Event	3rd International Conference on Advances in Physical Sciences and Materials: ICAPSM 2022 - Hybrid, Coimbatore, India Duration: 18 Aug 2022 → 19 Aug 2022

Publication series

Name	AIP Conference Proceedings
Number	1
Volume	2901
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	3rd International Conference on Advances in Physical Sciences and Materials: ICAPSM 2022
Country/Territory	India
City	Hybrid, Coimbatore
Period	18/08/22 → 19/08/22

Keywords

Deep Learning
Life Cycle Assessment
Plastic Bottle Packaging Products

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1063/5.0178584

Cite this

Fatriansyah, J. F., Thaqia, A. D., Rohman, M. S., Hartoyo, F., & Dhaneswara, D. (2023). Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products. In H. Shankar, P. Thangaraj, & K. Mohana Sundaram (Eds.), AIP Conference Proceedings (1 ed.). Article 060018 (AIP Conference Proceedings; Vol. 2901, No. 1). American Institute of Physics Inc.. https://doi.org/10.1063/5.0178584

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title = "Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products",

abstract = "Plastic waste is one of the problems that must be considered because it can negatively impact the environment. These problems can be above using the Life Cycle Assessment (LCA) approach to increase awareness regarding the environmental impact caused by plastic products. However, the LCA method is quite complex. So as to facilitate the work of users can be done by implementing LCA on plastic packaging products using a user-friendly system. In this study, the creation of a deep learning model algorithm to predict the LCA score of plastic bottle packaging products. The results obtained are that deep learning programs can be used in predicting LCA scores, even if there is a difference between the actual and predicted values.",

keywords = "Deep Learning, Life Cycle Assessment, Plastic Bottle Packaging Products",

author = "Fatriansyah, {Jaka Fajar} and Thaqia, {Aurelia Divanti} and Rohman, {Muhammad Syaikh} and Fernanda Hartoyo and Donanta Dhaneswara",

note = "Publisher Copyright: {\textcopyright} 2023 American Institute of Physics Inc.. All rights reserved.; 3rd International Conference on Advances in Physical Sciences and Materials: ICAPSM 2022 ; Conference date: 18-08-2022 Through 19-08-2022",

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language = "English",

series = "AIP Conference Proceedings",

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Fatriansyah, JF, Thaqia, AD, Rohman, MS, Hartoyo, F & Dhaneswara, D 2023, Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products. in H Shankar, P Thangaraj & K Mohana Sundaram (eds), AIP Conference Proceedings. 1 edn, 060018, AIP Conference Proceedings, no. 1, vol. 2901, American Institute of Physics Inc., 3rd International Conference on Advances in Physical Sciences and Materials: ICAPSM 2022, Hybrid, Coimbatore, India, 18/08/22. https://doi.org/10.1063/5.0178584

Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products. / Fatriansyah, Jaka Fajar; Thaqia, Aurelia Divanti; Rohman, Muhammad Syaikh et al.
AIP Conference Proceedings. ed. / H. Shankar; P. Thangaraj; K. Mohana Sundaram. 1. ed. American Institute of Physics Inc., 2023. 060018 (AIP Conference Proceedings; Vol. 2901, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products

AU - Fatriansyah, Jaka Fajar

AU - Thaqia, Aurelia Divanti

AU - Rohman, Muhammad Syaikh

AU - Hartoyo, Fernanda

AU - Dhaneswara, Donanta

PY - 2023/12/15

Y1 - 2023/12/15

N2 - Plastic waste is one of the problems that must be considered because it can negatively impact the environment. These problems can be above using the Life Cycle Assessment (LCA) approach to increase awareness regarding the environmental impact caused by plastic products. However, the LCA method is quite complex. So as to facilitate the work of users can be done by implementing LCA on plastic packaging products using a user-friendly system. In this study, the creation of a deep learning model algorithm to predict the LCA score of plastic bottle packaging products. The results obtained are that deep learning programs can be used in predicting LCA scores, even if there is a difference between the actual and predicted values.

AB - Plastic waste is one of the problems that must be considered because it can negatively impact the environment. These problems can be above using the Life Cycle Assessment (LCA) approach to increase awareness regarding the environmental impact caused by plastic products. However, the LCA method is quite complex. So as to facilitate the work of users can be done by implementing LCA on plastic packaging products using a user-friendly system. In this study, the creation of a deep learning model algorithm to predict the LCA score of plastic bottle packaging products. The results obtained are that deep learning programs can be used in predicting LCA scores, even if there is a difference between the actual and predicted values.

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KW - Life Cycle Assessment

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Deep Learning for Life Cycle Assessment (LCA) Score Prediction on Plastic Bottle Packaging Products

Abstract

Publication series

Conference

Keywords

UN SDGs

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