Data driven analysis using fuzzy time series for air quality management in Surabaya
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Published:
Dec 29, 2017
Keywords:
Fuzzy Time Series, forecasting, air pollution
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Abstract
One of the environmental issues that can affect human health is air pollution. As the second largest city in Indonesia, economic development and infrastructure construction in the city of Surabaya led to the increasing role of industrial and motor vehicle use which is proportional to the increase in fuel oil consumption. This condition ultimately led to declining air quality. Gas pollutants that contribute to air pollution such as CO, SO2, O3, NO2 and particulate matter PM10 are pollutants that have a direct impact on health. This study aims to analyze, monitor and predict air pollutant concentrations recorded by the Environment Agency Surabaya City based on time series with Fuzzy Time Series.MAPE calculation results on the parameters of pollutants are NO2: 23.6%, CO: 19.5%, O3: 22.75%, PM10: 9.96% and SO2: 3.6%.
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How to Cite
Darmawan, D., Irawan, M. I., & Syafei, A. D. (2017). Data driven analysis using fuzzy time series for air quality management in Surabaya. Sustinere: Journal of Environment and Sustainability, 1(2), 131–143. https://doi.org/10.22515/sustinere.jes.v1i2.13
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References
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Song, Q & Chissom B.S. (1993). Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54 (3), pp. 269 – 277.
Tauryawati, M. L., dan Irawan, M. I. (2014). Perbandingan Metode Fuzzy Time Series Cheng dan Metode Box-Jenkins untuk Memprediksi IHSG, Jurnal Sains dan Seni ITS, Vol. 3, No. 2, pp. A34-A39.
Zadeh L.A. (1978). Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets Syst, Vol. 1, No. 1, pp. 3-28.
Zhang, Z., and Zhu, Q. (2013). Fuzzy Time Series Forecasting Based On K-Means Clustering, Open Journal of Applied Sciences, Vol. 2, No. 04, pp. 100.
Environment Protection Authority, Government of Victoria (EPAV), Publication 421.
Alpaslan, F., and Cagcag, O. (2012). A seasonal fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering, Journal of social and Economic Statistics, Vol. 2, No.1, page. 1-13.
Akbar, S. and Irawan, M. I. (2016). Comparison of Sale Forecasting Seed Product with the Support Vector Regression and Fuzzy Time Series. Extended Abstrack Book of The 2nd Internasional Seminar on Science and Technology (ISST) for Sustainable Infrastructure, Department of Civil Engineering Postgraduate Program, ITS, Surabaya, page. 42-43.
Anan, K, Anupam and Yerpude, R. R. (2015). Application of Fuzzy Time Series Forecasting Model for Indian Underground Coal Mining Fatal Accident Data, International Journal of Engineering and Technology (IJET), Vol. 7, No. 2, pp. 719-726.
Anggriani, Darni (2012). Perbandingan Model Chen dan Model Lee Pada Metode Fuzzy Time Series Untuk Prediksi
Harga Emas, Tugas Akhir, Jurusan Teknik Informatika Universitas Islam Negeri Sultan Syarif Kasim Riau.
Babuska, R. (1999). “Data-Driven Fuzzy Modeling: Transparency and Complexity Issuesâ€, Proceedings European Symposium on Intelligent Techniques ESIT ’99, Orthodox Academy of Crete, Greece, page. AB-01.
Berutu, S. S., & Sediyono, E. (2013). Peramalan Penjualan Dengan Metode Fuzzy Time Series Haneen Talal Jasim, Prosiding Seminar Nasional Sains dan Aplikasi Komputasi (SENSAKOM), Fakultas Teknologi Informasi dan Fakultas Pertanian dan Bisnis, Universitas Kristen Satya Wacana.
Bocklisch, S. F., and Päßler, M. (2000). Fuzzy time series analysis, Fuzzy Control, Physica-Verlag HD, pp. 331-345.
BPS Kota Surabaya, 2015, Surabaya dalam Angka 2015, Kota Surabaya: Badan Pusat Statistik.
Casillas J, Cordon O, Herrera F., and Magdalena L. (2013). Accuracy Improvements in Linguistic Fuzzy Modeling. Springer.
Chen, S. M., & Hwang, J. R. (2000). Temperature prediction using fuzzy time series, IEEE Transactions on Systems,
Man, and Cybernetics, Part B (Cybernetics), Vol. 30, No. 2, pp. 263-275.
Dwiputra, I. R. & Syafei, A. D. (2015). Penerapan Time Series Forecasting dengan Metode ARIMA dan Linear Model Dalam Memprediksi Konsentrasi Kualitas Udara, Tugas Akhir, Jurusan Teknik Lingkungan Fakultas Teknik Sipil dan Perencanaan Institut Teknologi Sepuluh Nopember Surabaya, Surabaya.
Hakim, R. B., & Fitra, M. (2015). Metode Fuzzy Time Series Stevenson Porter dalam Meramalkan Konsumsi Batubata di Indonesia, Prosiding Seminar Nasional Matematika dan Pendidikan Matematika, Universitas Muhammadiyah Surakarta.
Hansun, S. (2012). Peramalan data IHSG menggunakan fuzzy time series. IJCCS-Indonesian Journal of Computing and Cybernetics Systems, Vol.: 6, No.: 2, page. 79-88.
Hickman, J., Hassel, D., Joumard, R., Samaras, Z., and Sorenson, S. (1999). Methodology for Calculating Transport Emissions and Energy Consumption, Publication of The National Academies of Sciences, Engineering and Medicine (internet), https://trid.trb.org/view/707881.
Kieso, E Donald. (2002). Intermediate Accounting. Tenth Edition. Erlangga.
Kusumadewi S., dan Purnomo H. (2004). Aplikasi Logika Fuzzy, untuk pendukung keputusan. Graha Ilmu.
Pemerintah Kota Surabaya, 2008, Peraturan Daerah Kota Surabaya Nomor 3 Tahun 2008 tentang Pengendalian Pencemaran Udara, Kota Surabaya: Pemerintah Kota Surabaya. (internet) http://peraturan.go.id/perda/kota-surabaya-nomor-3-tahun-2008-11e452bd54f21ce08561313634333035.html
Poulsen, J. (2009). Fuzzy Time Series Forecasting–Developing a new forecasting model based on high order fuzzy time series, AAUE, Semester: CIS, pp. 4.
Priyana, J dan Abadi, A. M. (2011). Peramalan suhu udara di Yogyakarta menggunakan model fuzzy, Prosiding Seminar Nasional Penelitian, Pendidikan dan Penerapan MIPA, Fakultas MIPA, Universitas Negeri Yogyakarta, pp. 253-260.
Rachmawansah, K. (2014). Average-Based Fuzzy Time Series Untuk Peramalan Kurs Valuta Asing (Studi Kasus Pada Nilai Tukar USD-IDR dan EUR-USD), Jurnal Mahasiswa Statistik, Vol. 2, No. 6, page. 413-416.
Ramadhan, N. dan Syafei, A. D. (2015), Penerapan Model Exponential Smoothing Holt Winter dan ARIMA dalam memprediksi Konsentrasi Pencemar Udara, Tugas Akhir, Jurusan Teknik Lingkungan Fakultas Teknik Sipil dan Perencanaan Institut Teknologi Sepuluh Nopember Surabaya, Surabaya.
Rismaharini, T. (2016). Profil Kota Surabaya Tahun 2016, Pemerintah Kota Surabaya, Surabaya.
Song, Q & Chissom B.S. (1993). Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54 (3), pp. 269 – 277.
Tauryawati, M. L., dan Irawan, M. I. (2014). Perbandingan Metode Fuzzy Time Series Cheng dan Metode Box-Jenkins untuk Memprediksi IHSG, Jurnal Sains dan Seni ITS, Vol. 3, No. 2, pp. A34-A39.
Zadeh L.A. (1978). Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets Syst, Vol. 1, No. 1, pp. 3-28.
Zhang, Z., and Zhu, Q. (2013). Fuzzy Time Series Forecasting Based On K-Means Clustering, Open Journal of Applied Sciences, Vol. 2, No. 04, pp. 100.