Application of ChatGPT Plus in Predictive Analysis of Particulate Matter Levels in Industrial Environment of Glucose Syrup and Maltodextrin
DOI:
10.29303/jppipa.v11i8.10853Published:
2025-08-25Downloads
Abstract
This study examines the variation of Particulate Matter levels in eight work areas and evaluates the correlation between Particulate Matter levels and measurement time. The observed areas include starch warehouse, starch damping, coal storage, finished goods, load out, maintenance workshop, chemical warehouse, and bagging house. Data collection was conducted periodically over six quarters from 2021 to 2022. The analysis results showed significant variations in Particulate Matter levels between work areas. A strong positive correlation was found in the ‘load out’ area (r=0.791), but it was not statistically significant (p=0.061). In contrast, the ‘starch warehouse’ area showed a strong negative correlation (r=-0.662), but was also not significant (p=0.152). The use of ChatGPT Plus in this study facilitated data analysis and prediction of Particulate Matter levels. This Artificial Intelligence technology is able to process historical data, perform exploratory analyses, and develop prediction models such as AutoRegressive Integrated Moving Average and linear regression. Further research with long-term data is needed to understand the dynamics of air pollution in industrial environments. This research contributes to the understanding of the impact of air quality on workers' health and demonstrates the potential use of AI technology in environmental data analysis and air pollution prediction.
Keywords:
Air quality ChatGPT plus Industrial pollution Particulate matterReferences
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