A PERFORMANCE COMPARISON OF DECISION TREE MODELS FOR PM10 PREDICTION IN JAKARTA English
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Abstract
PM10 are airborne particulates that have a diameter of ≤10 μm. The potential hazards of PM10 particulates are an issue that is being intensified by many researchers. This research utilizes PyCaret, a library to accelerate the process of modeling and experimentation in the field of machine learning (ML) and data science. This research compares the performance of three decision tree-based models Extra Trees, Random Forest, and XGBoost in predicting PM10 particulate levels, presenting data and visualizations for each models predictions. The data used is ISPU data at five air quality monitoring stations in Jakarta, with the main dataset of PM10 in 2021. The forecast results show an increasing graph pattern, with higher fluctuations in XGBoost. The Extra Trees model produces the best performance, with MASE 0.8808, RMSSE 0.8113, MAE 12.6173, RMSE 14.7436, MAPE 0.2433, SMAPE 0.207, and R² -1.2013.
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References
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[7] S. Chakraborty and S. Bhattacharya, “Application of XGBoost algorithm as a predictive tool in a CNC turning process,” Reports in Mechanical Engineering, vol. 2, no. 1, pp. 190–201, 2021, doi: https://doi.org/10.31181/rme2001021901b.
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[9] Y. Kristanto, T. Agustin, and F. R. Muhammad, “Pendugaan Karakteristik Awan berdasarkan Data Spektral Citra Satelit Resolusi Spasial Menengah Landsat 8 Oli/Tirs (Studi Kasus: Provinsi Dki Jakarta),” Jurnal Meteorologi Klimatologi Dan Geofisika, vol. 4, no. 2, pp. 42–50, 2017, doi: https://doi.org/10.36754/jmkg.v4i2.46.
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[13] F. N. Fajri, A. Tholib, and W. Yuliana, “Application of Machine Learning Algorithm for Determining Elective Courses in Informatics Study Program,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, pp. 485–496, 2022, doi: https://doi.org/10.28932/jutisi.v8i3.3990.
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[18] J. Garcia-Arismendiz, S. Huertas-Zúñiga, C. A. Lizárraga-Portugal, J. C. Quiroz-Flores, and Y. J. Garcia-Lopez, “Improving Demand Forecasting by Implementing Machine Learning in Poultry Production Company,” learning, vol. 8, p. 9, 2023, doi: https://doi.org/10.14445/22315381/IJETT-V71I2P205.
[19] S. Gutmann, C. Maget, M. Spangler, and K. Bogenberger, “Truck parking occupancy prediction: Xgboost-LSTM model fusion,” Frontiers in Future Transportation, vol. 2, p. 693708, 2021, doi: https://doi.org/10.3389/ffutr.2021.693708.
[20] R. K. Patel, A. Kumari, S. Tanwar, W.-C. Hong, and R. Sharma, “AI-empowered recommender system for renewable energy harvesting in smart grid system,” IEEE Access, vol. 10, pp. 24316–24326, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3152528.
[21] Y. Niu, “Walmart Sales Forecasting using XGBoost algorithm and Feature engineering,” in 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 2020, pp. 458–461. doi: https://doi.org/10.1109/ICBASE51474.2020.00103.
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[24] J.-W. Baek and K. Chung, “Multi-context mining-based graph neural network for predicting emerging health risks,” IEEE Access, vol. 11, pp. 15153–15163, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3243722.
[25] A. M. Iqbal, I. T. Setiadi, A. D. Pratama, and I. Imelda, “Stock Price Prediction of PT. Kimia Farma, Tbk Using Bayesian Ridge Algorithm,” Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan, vol. 17, no. 3, pp. 2218–2229, 2023, doi: http://dx.doi.org/10.35931/aq.v17i3.2222.
[26] S. R. P. Ariyanto and W. Yustanti, “Prediksi Kenaikan Jabatan Pranata Komputer pada Kementerian X dengan Menggunakan Model Algoritma Klasifikasi Linear Discriminant Analysis (LDA),” Journal of Emerging Information System and Business Intelligence (JEISBI), vol. 4, no. 3, pp. 40–49, 2023, [Online]. Available: https://ejournal.unesa.ac.id/index.php/JEISBI/article/view/54229