Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN
- Rizky Fauzi Ramadhani Telkom University, Bandung, Indonesia
-
Sri Suryani Prasetiyowati
*
Telkom University, Bandung,
Indonesia
- Yuliant Sibaroni Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
Jakarta is a city in Indonesia that has a high population density that must pay attention to its health condition. Good air quality provides positive benefits to support public health so that they can be more productive at work and create fresh and healthy air. This study uses Machine Learning to classify air based on certain attributes. Then, the development of a prediction model based on time data is designed to produce a predictive map of air pollution in Jakarta area for the next 3 years. The methods applied are Decision Tree and Artificial Neural Networks. As a result, the Decision Tree and Artificial Neural Network models show very good accuracy for predictions from 2024 to 2026. The Decision Tree and Artificial Neural Network models get an accuracy of 98% and 94%. In 2025 the Decision Tree and Artificial Neural Network models get 99% and 93% accuracy. In 2026 the Decision Tree and Artificial Neural Network models get an accuracy of 94% and 93% which can be seen from the Decision Tree model which is superior to the Artificial Neural Network with a difference of 1 - 6%.
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References
K. Naive et al., “Klasifikasi naive bayes untuk memprediksi kualitas udara berdasarkan indeks standar pencemar udara (ispu),” 2019.
H. Niska, T. Hiltunen, A. Karppinen, J. Ruuskanen, and M. Kolehmainen, “Evolving the neural network model for forecasting air pollution time series,” Eng. Appl. Artif. Intell., vol. 17, no. 2, pp. 159–167, 2004, doi: 10.1016/j.engappai.2004.02.002.
R. W. Gore and D. S. Deshpande, “An approach for classification of health risks based on air quality levels,” Proc. - 1st Int. Conf. Intell. Syst. Inf. Manag. ICISIM 2017, vol. 2017-Janua, pp. 58–61, 2017, doi: 10.1109/ICISIM.2017.8122148.
L. N. Araujo, J. T. Belotti, T. A. Alves, Y. de S. Tadano, and H. Siqueira, “Ensemble method based on Artificial Neural Networks to estimate air pollution health risks,” Environ. Model. Softw., vol. 123, no. April 2019, 2020, doi: 10.1016/j.envsoft.2019.104567.
A. Wang, J. Xu, R. Tu, M. Saleh, and M. Hatzopoulou, “Potential of machine learning for prediction of traffic related air pollution,” Transp. Res. Part D Transp. Environ., vol. 88, no. October, p. 102599, 2020, doi: 10.1016/j.trd.2020.102599.
Y. Gao, Z. Wang, C. yang Li, T. Zheng, and Z. R. Peng, “Assessing neighborhood variations in ozone and PM2.5 concentrations using decision tree method,” Build. Environ., vol. 188, no. June 2020, p. 107479, 2021, doi: 10.1016/j.buildenv.2020.107479.
S. M. Cabaneros, J. K. Calautit, and B. R. Hughes, “A review of artificial neural network models for ambient air pollution prediction,” Environ. Model. Softw., vol. 119, no. June 2018, pp. 285–304, 2019, doi: 10.1016/j.envsoft.2019.06.014.
M. W. Ahmad, M. Mourshed, and Y. Rezgui, “Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption,” Energy Build., vol. 147, pp. 77–89, 2017, doi: 10.1016/j.enbuild.2017.04.038.
B. Ekiz, O. Baygul, H. Yalcintan, and M. Ozcan, “Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids,” Meat Sci., vol. 161, p. 108011, 2020, doi: 10.1016/j.meatsci.2019.108011.
S. Moni, E. Aziz, A. P. P. Abdul Majeed, and M. Malek, “The prediction of blue water footprint at Semambu water treatment plant by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models,” Phys. Chem. Earth, vol. 123, no. December 2019, p. 103052, 2021, doi: 10.1016/j.pce.2021.103052.
T. S. Chang, “A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction,” Expert Syst. Appl., vol. 38, no. 12, pp. 14846–14851, 2011, doi: 10.1016/j.eswa.2011.05.063.
S. Zhou, J. Li, Y. Zhang, X. Liu, and W. Zhang, “Prediction of the ground temperature variations caused by the operation of GSHP system with ANN,” Geothermics, vol. 95, no. May, p. 102140, 2021, doi: 10.1016/j.geothermics.2021.102140.
T. Akhir, “KLASIFIKASI TINGKAT UDARA DKI JAKARA DENGAN Program Studi Sarjana Informatika Fakultas Informatika Universitas Telkom Bandung,” 2021.
W. Song, Y. Zhang, and S. C. Park, “A novel deep auto-encoder considering energy and label constraints for categorization,” Expert Syst. Appl., vol. 176, no. February, p. 114936, 2021, doi: 10.1016/j.eswa.2021.114936.
A. Performance, E. Alshdaifat, D. Alshdaifat, A. Alsarhan, F. Hussein, and S. Moh, “The Effect of Preprocessing Techniques , Applied to Numeric,” 2021.
D. Saripurna, A. Calam, Y. Yusnidah, and Z. Lubis, “Sistem Cerdas Pemanggang Jagung Semi Otomatis Berbasis Mikrokontroler Menggunakan Metode PWM (Pulse Width Modulation,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 18, no. 1, p. 82, 2019, doi: 10.53513/jis.v18i1.108.
A. I. Sang, E. Sutoyo, and I. Darmawan, “Analisis Data Mining Untuk Klasifikasi Data Kualitas Udara DKI Jakarta Menggunakan Algoritma Decision Tree Dan Support Vector Machine,” e-Proceeding Eng., vol. 8, no. 5, pp. 8954–8963, 2021.
F. ezzahra Salamate and J. Zahi, “Supervised learning: classification using decision trees for better practice in epidemiology Case study: The prevalence of tuberculosis,” Procedia Comput. Sci., vol. 201, pp. 783–788, 2022, doi: 10.1016/j.procs.2022.03.107.
B. Li, Y. Lee, W. Yao, Y. Lu, and X. Fan, “Development and application of ANN model for property prediction of supercritical kerosene,” Comput. Fluids, vol. 209, 2020, doi: 10.1016/j.compfluid.2020.104665.
F. Arias del Campo, M. C. Guevara Neri, O. O. Vergara Villegas, V. G. Cruz Sánchez, H. de J. Ochoa Domínguez, and V. García Jiménez, “Auto-adaptive multilayer perceptron for univariate time series classification,” Expert Syst. Appl., vol. 181, no. January, 2021, doi: 10.1016/j.eswa.2021.115147.
T. Le Minh, L. Van Tran, and S. V. T. Dao, “A Feature Selection Approach for Fall Detection Using Various Machine Learning Classifiers,” IEEE Access, vol. 9, pp. 115895–115908, 2021, doi: 10.1109/ACCESS.2021.3105581.
H. B. Chakrapani, S. Chouraisa, A. Saha, and J. N. Swathi, “Predicting Performance Analysis of System Configurations to Contrast Feature Selection Methods,” Int. Conf. Emerg. Trends Inf. Technol. Eng. ic-ETITE 2020, 2020, doi: 10.1109/ic-ETITE47903.2020.106.
S. Yadav and S. Shukla, “Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification,” Proc. - 6th Int. Adv. Comput. Conf. IACC 2016, no. Cv, pp. 78–83, 2016, doi: 10.1109/IACC.2016.25.
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