MODEL ADAPTIF NEURO FUZZY INFERENCE SYSTEM BERDASARKAN PH, TEMPERATUR DAN TSS UNTUK PREDIKSI NILAI COD
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Submitted
27 September 2017
Published
30 April 2017
Keywords:
-
Adaptif Neuro Fuzzy Inference System (ANFIS), Cemical Oxygen Demand (COD), Estimasi, Model, Kualitas Air Sungai
Abstract
Abstrak–- Tingkat pencemaran sungai setiap tahun mengalami peningkatan, hal ini menyebabkan kualitas air sungai semakin menurun yang disebabkan oleh buangan limbah industri dan limbah domestik. Untuk mendapatkan model estimasi Cemical Oxygen Demand (COD) maka perlu dilakukan analisa data di lingkungan sungai. Kemudian dilakukan pembentukan model estimasi untuk memprediksi Cemical Oxygen Demand (COD). Untuk menyelesaikan permasalahan tersebut maka diperlukan suatu algoritma untuk estimasi dan prediksi Cemical Oxygen Demand (COD) untuk mengetahui gambaran umum terhadap tingkat COD. Adaptif Neuro Fuzzy Inference System adalah suatu algoritma untuk menyelesaikan permasalahan tersebut. Hasil penelitian menunjukkan menggunakan Adaptif Neuro Fuzzy Inference System (ANFIS) dapat diterapkan untuk membangun model estimasi untuk prediksi nilai Cemical Oxygen Demand (COD. Hasil model estimasi yang dinilai paling baik dengan RMSE training 2,11993, RMSE testing 5,93532, MAE training 1,52225, MAE testing 4,2165 dengan persentase keberhasilan untuk training 94,7695 % dan testing 68,1839 %.
References
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[3] Anonim. (2001), Peraturan Pemerintah Republik Indonesia Nomor 82 Tahun 2001 tentang Kualitas dan Pengendalian Pencemaran Air.
[4] Anonim. (2009), Undang-Undang Republik Indonesia Nomor 32 Tahun 2009 tentang Perlindungan dan Pengelolaan Lingkungan Hidup.
[5] Anonim. (2010), Peraturan Menteri Negara Lingkungan Hidup Nomor 01 Tahun 2010 Tentang Tata Laksana Pengendalian Pencemaran Air.
[6] Anonim. (2010), Peraturan Gubernur Jawa Timur Nomor 61 Tahun 2010 tentang Penetapan Kelas Air Pada Air Sungai.
[7] Anonim, (2012), “Daya Dukung dan Daya Tampung Lingkungan di Sungai Kali Masâ€. Laporan Penelitian Badan Lingkungan Hidup Surabaya 2011
[8] Arifin Z. dan M.I. Irawan, (2009), “Adaptive Sensitivity Sensitivity-based Linear Learning Method Algorithms for Data Classificationâ€, Proceeding of 5th International Conference of Mathematics, Statistics and Their Aplications, Bukit Tinggi – West Sumatra Indonesia.
[9] Bramer, Max, (2007), †Principles of Data Mining “,Springer-Verlag, London.
[10] Barba-Brioso C., Fernández-Caliani J.C., Miras A. , Cornejo J. , Galán E, (2010), “Multi-source water pollution in a highly anthropized wetland system associated with the estuary of Huelva (SW Spain)“, Elsevier Journal of Marine Pollution Bulletin.
[11] Bao L.J., Maruya K A, Snyder S.A., and E Y. Zeng, (2011), “China’s water pollution by persistent organic pollutants,†Elsevier Journal of Environmental Pollution.
[12] Effendi, H. (2003), Telaah Kualitas Air Bagi Pengelolaan Sumber Daya dan Lingkungan Perairanâ€, Penerbit Kanisisus, Yogyakarta.
[13] Fausett, L, (1995), Fundamental of Neural Networks: Architecture, Algorithms, and Applications, Prentice Hall New Jersey
[14] Hanselman, Duane and Bruce Littlefield, (2001), Matlab-Bahasa Komputasi Teknis, Komputasi, Visualisasi dan Pemrograman (Terjemahan), Andi Offse,t Yogyakarta,
[15] Hasanuddin dan M.I. Irawan, (2009), “Sensitivity Analisis of Probabilistic Radial Basis Function Networksâ€, Proceeding of 5th International Conference on Mathematics, Statistics and Their Aplications, Bukit Tinggi – West Sumatra Indonesia.
[16] Haykin, Simon,(1999), Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey.
[17] Hazlina, (2013), An Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Survival, Thesis submitted to The University of Nottingham, Nottingham.
[18] Irawan M.I., Syaharuddin, M., Daryono Budi Utomo, dan A. Mustikarukmi, (2013), “Intelligent Irrigation Water Requirement System Based On Artificial Neural Networks and Profit Optimization for Planting Time Decision Making of Crops In Lombok Island†Journal of Applied and Theoretical Information Technology, Volume 58 No.3 December 31, 2013
[19] Irawan M.I. dan E. Satriyanto, (2008), “Virtual Pointer untuk Identifikasi Isyarat Tangan sebagai Pengendali Gerakan Robot Secara Real-Time, Jurnal Informatika Vol.9 No. 1 Mei 2008
[20] Irawan, M.I. dan A.B. Pratiwi, (2011), “A RBF- Egarch Neural Netwoks Model for Time Series Forecastingâ€, Proceeding of The International Conference on Numerical Analysis and Optimization (ICeMATH - 2011), Yogyakarta
[21] Irawan, M. I, (2010), “Pengembangan Metode Pembelajaran Cepat Pada Jaringan Syaraf Tiruan untuk Optimasi Waktu Dalam Stabilitas Bobot dan Minimalisasi Kesalahan Dalam Pengenalan Polaâ€, Laporan Hasil Penelitian Hibah Pascasarjana – LPPM ITS.
[22] Irawan, M.I, Apiliani, E dan Z. Darojah, (2008), “Using An Extended and Ensemble Kalman Filter For The Training of Feedforward Neural Network In Time Series Forecastingâ€, in Proceeding of 3rd- International Conference On Mathematics And Statistics (ICoMS 3), Bogor, Juli 2-4.
[23] Jong, JS., Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab, Andi Offset, Yogyakarta.
[24] JSR jang, CT Sun, E Mizutami, (1997), Neuro Fuzzy and Soft Computing, PTR Prentice Hall
[25] Keputusan Menteri Negara Lingkungan Hidup Nomor 112 Tahun 2013 tentang Baku Mutu Air Limbah Domestik.
[26] Kusumadewi, S. (2001), Artificial Intelligence (Teknik dan Aplikasinya), Graha Ilmu, Yogyakarta.
[27] Kusumadewi, S. (2004), Membangun Jaringan Syaraf Tiruan menggunakan Matlab dan Excel Link, Graha Ilmu, Yogyakarta.
[28] Li, C and Diebold, F.X, (2006),†Forcasting the Term Structure of Government Bond Yieldâ€, Journal of Econometrics,130,337-364.
[29] Masduqi, A dan E. Apriliani, (2008), “Estimation of Surabaya River Water Quality Using Kalman Filter Algorithmâ€, IPTEK, The Journal for Technology and Science, Vol. 19, No. 3, August 2008
[30] Masduqi A, (2012), Operasi dan Proses Pengolahan Air, ITS Press
[31] Murat Ay, Ozgur Kisi (2014), “Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniquesâ€, Journal of Hydrolog, 511 (2014) 279–289
[32] Nastos, P.T., Moustris, K.P., Larissi, I.K., Paliatsos, A.G. (2011), â€Rain Intensity Forecast Using Artificial Neural Network in Athens, Greeceâ€, Atmospheric Research, Vol 119, Hal. 153 - 160.
Authors
[1]
W. Harianto, “MODEL ADAPTIF NEURO FUZZY INFERENCE SYSTEM BERDASARKAN PH, TEMPERATUR DAN TSS UNTUK PREDIKSI NILAI COD”, SMARTICS, vol. 3, no. 1, pp. 12–16, Apr. 2017.
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