Estimasi Konsentrasi PM10 Menggunakan Support Vector Regression
DOI:
https://doi.org/10.30595/jrst.v6i1.8977Keywords:
PM10, Support Vector Regression, Kualitas Udara, Estimasi, Kabut AsapAbstract
PM10 berkontribusi terhadap polusi udara pada saat kejadian kabut asap di musim kemarau dengan salah satu sumber utamanya adalah pembakaran biomassa. Pada saat musim kemarau, terdapat banyak kegiatan pembersihan lahan di Mempawah untuk persiapan masa tanam yang Sebagian besar dilakukan dengan pembakaran sisa tanaman. Sebagai salah satu polutan utama yang dapat memengaruhi kesehatan manusia, maka estimasi konsentrasi PM10 sangat penting untuk dilakukan. Tujuan dari penelitian ini untuk melakukan estimasi konsentrasi PM10 di Mempawah tahun 2019 menggunakan Support Vector Regression (SVR) berdasarkan data PM10 dan variabel meteorologi seperti curah hujan, kelembaban, suhu, tekanan permukaan laut dan kecepatan angin dari Stasiun Klimatologi Mempawah dengan periode latih tahun 2016 hingga 2018. Hasil penelitian ini menunjukkan bahwa estimasi konsentrasi PM10 menggunakan SVR berdasarkan parameter cuaca dapat menggambarkan variabilitas konsentrasi harian PM10 di Mempawah dengan baik, terkecuali saat terjadi kenaikan konsentrasi yang sangat tinggi yang mungkin dipengaruhi oleh faktor antropogenik. Selain itu, berdasarkan verifikasi, RMSE yang dihasilkan model estimasi hampir sama dengan nilai standar deviasi observasinya.Â
References
Arisman, A. (2020). Analisis Tren Kebakaran Hutan dan Lahan di Indonesia Periode Tahun 2015-2019. JURNAL SAINS TEKNOLOGI & LINGKUNGAN, 6(1), 1-9.
CAMS, C. A. M. S. (2020). Verification plots: documentation. Retrieved from http://macc-raq-op.meteo.fr/doc/userguideverificationstatistics.pdf
Ceperic, E., Ceperic, V., & Baric, A. (2013). A strategy for short-term load forecasting by support vector regression machines. IEEE Transactions on Power Systems, 28(4), 4356-4364.
Chantara, S. (2012). PM10 and its chemical composition: a case study in Chiang Mai, Thailand. Air quality-monitoring and modeling, 205-230.
Chumney, F. (2019). z-Scores. Retrieved from https://www.westga.edu/academics/research/vrc/assets/docs/zScores_HANDOUT.pdf
De Paz, J. F., Pérez, B., González, A., Corchado, E., & Corchado, J. M. (2010). A support vector regression approach to predict carbon dioxide exchange. In Distributed Computing and Artificial Intelligence (pp. 157-164): Springer.
EPA, E. P. A. (2019). Particulate Matter (PM) in New England. Retrieved from https://www3.epa.gov/region1/airquality/partic.html
Heil, A., & Goldammer, J. (2001). Smoke-haze pollution: a review of the 1997 episode in Southeast Asia. Regional Environmental Change, 2(1), 24-37.
Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector classification"(http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf).
Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support vector machines in R. Journal of statistical software, 15(9), 1-28.
Lu, W.-Z., & Wang, W.-J. (2005). Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere, 59(5), 693-701.
Lu, W., Wang, W., Fan, H., Leung, A., Xu, Z., Lo, S., & Wong, J. (2002). Prediction of pollutant levels in causeway bay area of Hong Kong using an improved neural network model. Journal of environmental engineering, 128(12), 1146-1157.
Mareta, L., Hidayat, R., Hidayati, R., & Latifah, A. L. (2020). Pengaruh Faktor Alami dan Antropogenik Terhadap Luas Kebakaran Hutan dan Lahan di Kalimantan. Jurnal Tanah dan Iklim, 43(2), 147-155.
Martin, E. P. (2011). Comparative Performance of Different Statistical Models for Predicting Ground-Level Ozone (O3) and Fine Particulate Matter (PM2. 5) Concentrations in Montréal, Canada. Concordia University,
Meilantika, A. D., Hadisaputro, S., & Setiawati, M. (2018). Faktor Risiko Host dan Environment yang Berpengaruh Terhadap Kejadian Pneumonia Pada Balita (Studi di Wilayah Kerja Puskesmas Rawat Jalan Wajok Hulu Kecamatan Siantan Kabupaten Mempawah). School of Postgraduate,
Meyer, D., & Wien, F. T. (2015). Support vector machines. The Interface to libsvm in package e1071, 28.
Munir, M., Akbar, A. R., Badaruddin, B., & Wahdah, R. (2018). Hubungan Cuaca dan Konsentrasi Pm10 (Studi Kasus di Kota Banjarbaru). EnviroScienteae, 14(1), 46-61.
Murphy, A. H. (1988). Skill scores based on the mean square error and their relationships to the correlation coefficient. Monthly weather review, 116, 2417-2424.
Nugroho, A. S., Witarto, A. B., & Handoko, D. (2003). Support vector machine teori dan aplikasinya dalam bioinformatika. Kuliah Umum Ilmu Komputer. com.
RRI.co.id. (2019). Karhutla Mempawah Sudah Memakan Lahan Seluas 473 Hektar. Retrieved from http://rri.co.id/post/berita/705918/mitigasi_bencana/karhutla_mempawah_sudah_memakan_lahan_seluas_473_hektar.html
Sánchez, A. S., Nieto, P. G., Fernández, P. R., del Coz Díaz, J., & Iglesias-Rodríguez, F. J. (2011). Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Mathematical and Computer Modelling, 54(5-6), 1453-1466.
Suarapemredkalbar.com. (2019). Kualitas Udara Mempawah Berbahaya. Retrieved from https://www.suarapemredkalbar.com/berita/mempawah/2019/09/19/kualitas-udara-mempawah-berbahaya
Tacconi, L. (2003). Kebakaran hutan di Indonesia: penyebab, biaya dan implikasi kebijakan: CIFOR.
Ul-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Rosaida, N., & Hamid, H. A. (2013). Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmospheric Environment, 77, 621-630.
Usman, H., & Akbar, R. P. S. (2000). Pengantar Statistika. Jakarta: Bumi Aksara.
Vapnik, V. (2013). The nature of statistical learning theory: Springer science & business media.
Walther, B. A., & Moore, J. L. (2005). The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28, 815-829.
Wang, W., Men, C., & Lu, W. (2008). Online prediction model based on support vector machine. Neurocomputing, 71(4-6), 550-558.
Wang, X., & Zhong, Y. (2003). Statistical learning theory and state of the art in SVM. Paper presented at the The Second IEEE International Conference on Cognitive Informatics, 2003. Proceedings.
Weizhen, H., Zhengqiang, L., Yuhuan, Z., Hua, X., Ying, Z., Kaitao, L., . . . Yan, M. (2014). Using support vector regression to predict PM10 and PM2. 5. Paper presented at the IOP Conference Series: Earth and Environmental Science.
WHO, W. H. O. (2003). Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. Retrieved from https://apps.who.int/iris/handle/10665/107478
Yulianti, N. (2018). Pengenalan bencana kebakaran dan kabut asap lintas batas (studi kasus eks proyek lahan gambut sejuta hektar): Bogor: IPB Press.
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Zia Ayu Frakusya, Rista Hernandi Virgianto, Muhammad Elifant Yuggotomo

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access)
JRST (Jurnal Riset Sains dan Teknologi) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.