Implementation Of Support Vector Regression In Prediction Air Quakity Index In Banda Aceh City
Keywords:
Support Vector Regression, Machine Learning, Air Quality IndexAbstract
Air quality is one of the important aspects in maintaining environmental balance and public health. Increasing air quality in the environment is a matter of concern. Therefore, a method that can predict the Air Quality Index (AQI) effectively is needed to be able to monitor and support decision making on environmental impacts. This study aims to predict the Air Quality Index in Banda Aceh City using the Support Vector Regression algorithm, with five main parameters used in the study, namely particulate matter , Sulfur dioxide, Nitrogen dioxide, Carbon monoxide , and Ozone . In this research, the Support Vector Regression algorithm was chosen because of its ability to handle non-linear data and also because it can provide accurate predictions on data. The prediction system designed will be web-based using the flask framework and MySQL database, while the Support Vector Regression modeling will be done on google colab for the media used. In the process of modeling the data will be divided into 80% training data and 20% test data to ensure the model can capture long and short-term patterns. The results of the prediction will be compared using the Root Mean Squarred Error (RMSE) and Mean Squarred Error (MSE) evaluation metrics. The results of the evaluation using both metrics yielded RMSE values of 1.9001 and MSE of 3.6015. These values indicate good performance of the model in predicting the data. This research is expected to provide insight for future similar research in terms of prediction using the Support Vector Regression algorithm.
References
[1] D. Kartikasari, “Analisis Faktor-Faktor Yang Mempengaruhi Level Polusi Udara Dengan Metode Regresi Logistik Biner,” MATHunesa J. Ilm. Mat., vol. 8, no. 1, pp. 55–59, 2020, doi: 10.26740/mathunesa.v8n1.p55-59.
[2] A. Riyanto, A. Maheswara, R. Zulianty, V. M. Alegra, and ..., “Tanggung Jawab Pemerintah dalam Penyelesaian Masalah Polusi Udara di DKI Jakarta,” J. Pendidik. Tambusai, vol. 7, no. 3, pp. 27890–27896, 2023, [Online]. Available: https://www.jptam.org/index.php/jptam/article/view/11232%0Ahttps://www.jptam.org/index.php/jptam/article/download/11232/8850
[3] A. Nata and S. Suparmadi, “Analisis Sistem Pendukung Keputusan Dengan Model Klasifikasi Berbasis Machine Learning Dalam Penentuan Penerima Program Indonesia Pintar,” J. Sci. Soc. Res., vol. 5, no. 3, p. 697, 2022, doi: 10.54314/jssr.v5i3.1041.
[4] R. E. Cahyono, J. P. Sugiono, and S. Tjandra, “Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen,” JTIM J. Teknol. Inf. dan Multimed., vol. 1, no. 2, pp. 106–116, 2019, doi: 10.35746/jtim.v1i2.22.
[5] H. Liu, Q. Li, D. Yu, and Y. Gu, “Air quality index and air pollutant concentration prediction based on machine learning algorithms,” Appl. Sci., vol. 9, no. 19, 2019, doi: 10.3390/app9194069.
[6] J. Wang, X. Li, L. Jin, J. Li, Q. Sun, and H. Wang, “An air quality index prediction model based on CNN-ILSTM,” Sci. Rep., vol. 12, no. 1, pp. 1–16, 2022, doi: 10.1038/s41598-022-12355-6.
[7] E. Alpaydin, Alpaydin, E., 2020. Introduction to machine learning. MIT press. 2020.
[8] Mahesh, “Mahesh, B., 2020. Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), pp.381-386.,” Int. J. Sci. Res., vol. 9(1), no. pp.381-386., 2020.
[9] N. Lavesson and P. Davidsson, “Quantifying the impact of learning algorithm parameter tuning,” Proc. Natl. Conf. Artif. Intell., vol. 1, no. 1, pp. 395–400, 2006.
[10] S. Pakpahan, A. Faâ, and others, “Sistem Informasi Pengelolaan Dana Desa Pada Desa Hilizoliga Berbasis Web,” J. Tek. Inform. UNIKA St. Thomas, pp. 109–117, 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Rizky Fasya Ramdhani, Rozzi Kesuma Dinata, Ar Razi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright Notice
Authors published in this journal agree to the following terms:
1. The copyright of each article is retained by the author (s).
2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.
5. Articles and all related material published are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.
