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

Aini, N., & Mustafa, M. S. (2020). Data Mining Approach to Predict Air Pollution in Makassar. 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS), 1–5. https://doi.org/10.1109/ICORIS50180.2020.9320800
  Google Scholar

Alamsyah, A., & Salma, N. (2018). A Comparative Study of Employee Churn Prediction Model. 2018 4th International Conference on Science and Technology (ICST). https://doi.org/10.1109/ICSTC.2018.8528586
  Google Scholar

Alexandra L’Heureux, Katarina Grolinger, Hany F. ElYamany, & Miriam A. M. Capretz. (2019). Machine Learning with Big Data.
  Google Scholar

Anantha Krishna, V., Koganti, H., Madhumathi, M., & Dharani, V. (2023). Air Quality Prediction Using Machine Learning Algorithms. Chem. Bull, 2023(S3), 2201–2206. https://doi.org/10.31838/ecb/2023.12.s3.275
  Google Scholar

Anshari, M., Syafrudin, M., Tan, A., Fitriyani, N. L., & Alas, Y. (2023). Optimisation of Knowledge Management (KM) with Machine Learning (ML) Enabled. In Information (Switzerland) (Vol. 14, Issue 1). MDPI. https://doi.org/10.3390/info14010035
  Google Scholar

Asfilia Nova Anggraini, Nisa Kholifatul Ummah, Yessy Fatmasari, & Khadijah Fahmi Hayati. (2022). Air Quality Forecasting in DKI Jakarta Using Artificial Neural Network. Journal of Computer Science and Information Technology, 14, No. 1, pp 33–40, 33–40.
  Google Scholar

Bilquise, G., & Khaled Shaalan, U. (2022). AI-based Academic Advising Framework: A Knowledge Management Perspective. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 13, Issue 8). www.ijacsa.thesai.org
  Google Scholar

Elvin, E., & Wibowo, A. (2024). Forecasting water quality through machine learning and hyperparameter optimization. Indonesian Journal of Electrical Engineering and Computer Science, 33(1), 496. https://doi.org/10.11591/ijeecs.v33.i1.pp496-506
  Google Scholar

Hai, P. M., Tinh, P. H., Son, N. P., Van Thuy, T., Hanh, N. T. H., Sharma, S., Hoai, D. T., & Duy, V. C. (2022). Mangrove health assessment using spatial metrics and multi-temporal remote sensing data. PLoS ONE, 17(12 December). https://doi.org/10.1371/journal.pone.0275928
  Google Scholar

Jubeile Mark Baladjay, Nisce Riva, Ladine Ashley Santos, Dan Michael Cortez, Criselle Centeno, & Ariel Antwaun Rolando Sison. (2023). Performance evaluation of random forest algorithm for automating classification of mathematics question items. World Journal of Advanced Research and Reviews, 18(2), 034–043. https://doi.org/10.30574/wjarr.2023.18.2.0762
  Google Scholar

Kang, G. K., Gao, J. Z., Chiao, S., Lu, S., & Xie, G. (2018). Air Quality Prediction: Big Data and Machine Learning Approaches. International Journal of Environmental Science and Development, 9(1), 8–16. https://doi.org/10.18178/ijesd.2018.9.1.1066
  Google Scholar

Kementrian Lingkungan Hidup dan Kehutanan Republik Indonesia. (2020). INDEKS STANDAR PENCEMAR UDARA.
  Google Scholar

Pakki Pavan Sai, R. R. ,Maila V. B. P. V. (2022). Prediction of Air Quality Index by Using Machine Learning. Journal of Architecture and Civil Engineering, 7(11), 01–07.
  Google Scholar

Pisoni, G., Molnár, B., & Tarcsi, Á. (2023). Knowledge Management and Data Analysis Techniques for Data-Driven Financial Companies. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-023-01607-z
  Google Scholar

Sankaravadivel, V., Thalavaipillai, S., Rajeswar, S., & Ramalingam, P. (2023). Feature based analysis of endometriosis using machine learning. Indonesian Journal of Electrical Engineering and Computer Science, 29(3), 1700–1707. https://doi.org/10.11591/ijeecs.v29.i3.pp1700-1707
  Google Scholar

Schaefer, C., & Makatsaria, A. (2021). Framework of Data Analytics and Integrating Knowledge Management. International Journal of Intelligent Networks, 2, 156–165. https://doi.org/10.1016/j.ijin.2021.09.004
  Google Scholar

Simu, S., Turkar, V., Martires, R., Asolkar, V., Monteiro, S., Fernandes, V., & Salgaoncary, V. (2020). Air Pollution Prediction using Machine Learning. 2020 IEEE Bombay Section Signature Conference (IBSSC), 231–236. https://doi.org/10.1109/IBSSC51096.2020.9332184
  Google Scholar

Somashekar, H., & Boraiah, R. (2023). Network intrusion detection and classification using machine learning predictions fusion. Indonesian Journal of Electrical Engineering and Computer Science, 31(2), 1147–1153. https://doi.org/10.11591/ijeecs.v31.i2.pp1147-1153
  Google Scholar

Taherdoost, H., & Madanchian, M. (2023). Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation. In Computers (Vol. 12, Issue 4). MDPI. https://doi.org/10.3390/computers12040072
  Google Scholar

Tangwannawit, S., & Tangwannawit, P. (2022). An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture. IAES International Journal of Artificial Intelligence, 11(1), 201–209. https://doi.org/10.11591/ijai.v11.i1.pp201-209
  Google Scholar

Yarragunta, S., Nabi, M. A., Jeyanthi, P., & Revathy, S. (2021). Prediction of air pollutants using supervised machine learning. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021, 1633–1640. https://doi.org/10.1109/ICICCS51141.2021.9432078
  Google Scholar

Download


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



Statistics

Abstract views: 274
PDF downloads: 114


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.