KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL

Siti ROHAJAWATI

siti.rohajawati@bakrie.ac.id
a:1:{s:5:"en_US";s:17:"Bakrie University";} (Indonesia)

Hutanti SETYODEWI


(Indonesia)
https://orcid.org/0009-0008-3937-652X

Ferryansyah Muji Agustian TRESNANTO


Bakrie University (Indonesia)
https://orcid.org/0009-0007-7830-7415

Debora MARIANTHI


Bakrie University (Indonesia)
https://orcid.org/0009-0002-8610-1357

Maruli Tua Baja SIHOTANG


PT. Festino Indonesia (Indonesia)
https://orcid.org/0000-0002-9348-5400

Abstract

This study utilizes knowledge management (KM) to highlight a documentation-centric approach that is enhanced through artificial intelligence. Knowledge management can improve the decision-making process for predicting models that involved datasets, such as air pollution. Currently, air pollution has become a serious global issue, impacting almost every major city worldwide. As the capital and a central hub for various activities, Jakarta experiences heightened levels of activity, resulting in increased vehicular traffic and elevated air pollution levels. The comparative study aims to measure the accuracy levels of the naïve bayes, decision trees, and random forest prediction models. Additionally, the study uses evaluation measurements to assess how well the machine learning performs, utilizing a confusion matrix. The dataset’s duration is three years, from 2019 until 2021, obtained through Jakarta Open Data. The study found that the random forest achieved the best results with an accuracy rate of 94%, followed by the decision tree at 93%, and the naïve bayes had the lowest at 81%. Hence, the random forest emerges as a reliable predictive model for prediction of air pollution.


Keywords:

knowledge management, air pollution, naïve bayes, decision tree, random forest

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Published
2024-03-30

Cited by

ROHAJAWATI, S., SETYODEWI, H., TRESNANTO, F. M. A., MARIANTHI, D., & SIHOTANG , M. T. B. (2024). KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL. Applied Computer Science, 20(1), 173–188. https://doi.org/10.35784/acs-2024-11

Authors

Siti ROHAJAWATI 
siti.rohajawati@bakrie.ac.id
a:1:{s:5:"en_US";s:17:"Bakrie University";} Indonesia

Authors

Hutanti SETYODEWI 

Indonesia
https://orcid.org/0009-0008-3937-652X

She is a lecturer at Bakrie University


Authors

Ferryansyah Muji Agustian TRESNANTO 

Bakrie University Indonesia
https://orcid.org/0009-0007-7830-7415

He is a student at Bakrie University


Authors

Debora MARIANTHI 

Bakrie University Indonesia
https://orcid.org/0009-0002-8610-1357

She is a student at Bakrie University


Authors

Maruli Tua Baja SIHOTANG  

PT. Festino Indonesia Indonesia
https://orcid.org/0000-0002-9348-5400

He is a IT consultant at PT Festino, Indonesia



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