PREDICTION OF AIR QUALITY INDEX USING DECISION TREE WITH DISCRETIZATION
DOI:
https://doi.org/10.51630/ijes.v3i3.82Keywords:
Air quality index, Decision tree, Repeated k-Fold Cross ValidationAbstract
Air quality is indicated by the Air Quality Index (AQI). Prediction or classification of AQI is an important research issue because it can impact many factors, such as the environment, health, transportation, agriculture, plantations, tourism, and education. The purpose of this study is to predict AQI using a decision tree. The results of calculating the performance of the decision tree method that implements the discretization technique show that this method is very good at predicting air quality, as indicated in particular by the Average Accuracy value of 99.05%, Macro Precision of 78.59%, and Macro Recall of 77.46%.
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References
A. Mayssara A. Abo Hassanin Supervised, "Udara," Pap. Knowl. . Towar. a Media Hist. Doc., pp. 6–35, 2014.
H. W. Isramadhanti, “Gambaran Kualitas Udara di Kota Yogyakarta Berdasarkan Pemantauan Air Quality Monitoring System tahun 2019-2020,” Skripsi. Politek. Kesehat. Kemenkes Yogyakarta, pp. 30–48, 2019.
J. Prayudha, A. Pranata, and A. Al Hafiz, “Implementasi Metode Fuzzy Logic Untuk Sistem Pengukuran Kualitas Udara Di Kota Medan Berbasis Internet of Things (Iot),” Jurteksi, vol. 4, no. 2, pp. 141–148, 2018, doi: 10.33330/jurteksi.v4i2.57.
Liu, H., Li, Q., Yu, D., Gu, Y., 2019. Air quality index and air pollutant concentration prediction based on machine learning algorithms, Applied Scince, 9, 4069, doi: 10.3390/app9194069.
Jia, M., Cheng, X., Zhao, T., Yin, C., Zhang, X., Wu., X, Wang, L., Zhang, R. (2019), Regional air quality forecast using a machine learning method and the WRF model over the Yangtze River Delta, East China. Aerosol and Air Quality Research, 19, pp. 1602-1613
Podviezko, A., Podvezko, V., 2015. Influence of dta transformation on multicriteria evaluation, Procedia Engineering, 122, pp.151-157.
Kresnawati, E. S., Resti, Y., Suprihatin, B., Kurniawan, M. R., & Amanda, W. A. (2021a). Coronary Artery Disease Prediction Using Decision Trees and Multinomial Naïve Bayes with k-Fold Cross Validation. Inomatika, 3(2), 174–189. https://doi.org/10.35438/inomatika.v3i2.266
García, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. In J. Kacprzyk & L. C. Jain (Eds.), Intelligent Systems Reference Library (72nd ed., Vol. 72). Springer Cham Heidelberg New York Dordrecht London. https://doi.org/10.1007/978-3-319-10247-4
Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. In Complementary literature None (2nd ed.). Morgan Kaufmann Publishers. https://www.elsevier.com/books/data-mining/witten/978-0-12-088407-0
Han, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techniques. Morgan Kaufmann Publishers. https://doi.org/https://doi.org/10.1016/C2009-0-61819-5
Chandra, W., Resti, Y., Suprihatin, B., 2022. Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality, Inomatika.
Resti, Y., Irsan, C., Putri, M. T., Yani, I., Anshori, and Suprihatin, B. (2022a). Identification of Corn Plant Diseases and Pests Based on Digital Images using, Science and Technology Indonesia, 7(1), 29–35, https://doi.org/10.26554/sti.2022.7.1.29-35.
Resti, Y., Irsan, C., Amini, M., Yani, I., Passarella, R., Zayanti, D. A. (2022b). Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests, Science and Technology Indonesia, 7(3), 284–290, https://doi.org/10.26554/sti.2022.7.3.284-290.
M. Sokolova and G. Lapalme, "A systematic analysis of performance measures for classification tasks," Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009.
Dinesh, S., & Dash, T. (2016). Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data. 1. http://arxiv.org/abs/1612.00671
Burger, S.V., Introduction to Machine Learning with R, 2018. Oreilly. United State of America. 978-1-491-97644-9 [LSI].
James, G., Daniela, W., Trevor, H., & Robert, T. (2013). An Introduction to Statistical Learning with Applications in R. In G. Casella, S. Fienberg, & I. Olkin (Eds.), Springer Texts in Statistics (1st ed., Vol. 1). Springer New York Heidelberg Dordrecht London. https://doi.org/10.1007/978-1-4614-7138-7
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