Knowledge management approach in comparative study of air pollution prediction model

• Autorzy: Rohajawati Siti , Setyodewi Hutanti , Tresnanto Ferryansyah Muji Agustian , Marianthi Debora , Sihotang Maruli Tua Baja

Identyfikatory

DOI

10.35784/acs-2024-11

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no 1
• Strony: 173--188
• Opis fizyczny: Bibliogr. 28 poz., fig., tab.

Twórcy

autor

Rohajawati Siti

• Bakrie University, Faculty of Engineering and Computer Science, Information System, Indonesia

• https://orcid.org/0000-0002-6775-8997

autor

Setyodewi Hutanti

• Bakrie University, Faculty of Engineering and Computer Science, Information System, Indonesia

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

autor

Tresnanto Ferryansyah Muji Agustian

• Bakrie University, Faculty of Engineering and Computer Science, Information System, Indonesia

• https://orcid.org/0009-0007-7830-7415

autor

Marianthi Debora

• Bakrie University, Faculty of Engineering and Computer Science, Information System, Indonesia

• https://orcid.org/0009-0002-8610-1357

autor

Sihotang Maruli Tua Baja

• PT. Festino Indonesia. IT Solution Architect, Indonesia

• https://orcid.org/0000-0002-9348-5400

Bibliografia

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