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dc.contributor.authorSuprayogi, Imam
dc.date.accessioned2012-06-21T04:45:07Z
dc.date.available2012-06-21T04:45:07Z
dc.date.issued2010-01
dc.identifier.citationAnonim, 1976, Salt Distribution in Estuaries, Rijkwaterstaat Communications no. 26. Government Publishing Office, Ne-therlands. Anwar, N. ,1998, Environmental Hydraulic Aspects in Lamong River and Fish Ponds, Dissertation, Toyo University. Demuth, H. & Beale.M., 1998 , Neural Network Toolbox for Use in Matlab, Math Work Inc, United, State of America. Dinas Hidrooseanografi., 1988, Daftar Pasang Surut Kepulauan Indonesia, Jawatan Hidrooseanografi Tentara Nasional Indo-nesia Angkatan Laut Republik Indonesia, Jakarta. Fairbridge,R., 1980, The Estuary : Its Definition and Geodyna-mic Cycle, dalam Chemistry and Biochemistry of Estuaries (ed). Olausson dan Cato, Wiley & Sons, New York. Fausset, L., 1996, Fundamentals of Neural Networks, Architec-tures, Algorithms, and Applications, Prentice Hall, Upper Saddle River, New Jersey. Gelley, N. & Jang, R., 2000, Fuzzy Logic Toolbox for Use With Matlab The Math Work Inc, New York . Iriawan, N., 2005, Pengembangan Simulasi Stokhastik Dalam Statistika Komputasi Data Driven, Pidato Pengukuhan Untuk Jabatan Guru Besar Dalam Bidang Statistik Komputasi dan Proses Stokhastik Pada Jurusan Statistik Fakultas Matema-tika dan Ilmu Pengetahuan Alam (MIPA) Institut Teknologi Sepuluh Nopember, Surabaya. Isnugroho., 1988, Penanggulangan Pengaruh Air Asin Di Muara Bengawan Solo. Prosiding Seminar Hidraulika dan Hidro-logi Wilayah Pantai Pusat Antar Universitas (PAU) Ilmu Teknik Universitas Gadjah Mada (UGM), Jogya-karta, 7-8 Nopember 1988. Jang, J.S.R., 1993, ANFIS: Adaptive Network Based Fuzzy Inference System, Journal IEEE Transaction on System Man and Cybernetic 23 no 3 : 665- 685. Jang, J.S.R., Sun C.T. & Mizutani, E., 1997, Neuro Fuzzy and Soft Computing, Prentice Hall, London. Legowo, S., 1998, Pengkajian Pendangkalan Muara Sungai Di Pantai Utara Pulau Jawa Barat dan Rekayasa Pemecahan-nya, Laporan Akhir Riset Unggulan Terpadu (RUT III/3) Lembaga Penelitian Institut Teknologi Bandung (ITB), Bandung. Odum, E.P.V., 1969, The Strategy of Ecosystem Development, Journal of Science164 : 262-270. Pratikto, W.A., 1999, Aplikasi Pemodelan Di Teknik Kelautan, Pidato Pengukuhan Untuk Jabatan Guru Besar Dalam Bidang Aplikasi Numerik dan Mekanika Fluida Pada Jurusan Teknik Kelautan Fakultas Teknik Kelautan (FTK) Institut Teknologi Sepuluh Nopember, Surabaya. Pribowo, W., 2000, Studi Mengenai Sedimentasi Muara Kali Po-rong, Tesis Master, Jurusan Teknik Sipil Bidang Keahli-an Manajemen Dan Rekayasa Sumberdaya Air, Institut Tekno-logi Sepuluh Nopember (ITS), Surabaya. Pritchard,D., 1967, Observation of Circulation In Coastal Plain Estuaries, dalam Estuaries eds. G.Lauff, American Assso-ciation for Advance of Science Publish. 83 : 37-44, Washing-ton D.C. Purnomo,M.H., 2004, Teknologi Soft Computing:Prospek dan Implementasinya Pada Rekayasa Medika dan Elektrik, Pidato Pengukuhan Untuk Jabatan Guru Besar Dalam Ilmu Artificial Intelligent Pada Fakultas Teknologi Indus-tri Institut Tekno-logi Sepuluh Nopember (ITS), Surabaya. Suyanto., 2008, Softcomputing Membangun Mesin Ber-IQ Ting-gi, Informatika, Bandung. Suprayogi, I., 2009, Model Peramalan Intrusi Air Laut Di Estuari Menggunakan Pendekatan Softcomputing. Diser-tasi Doktor, Jurusan Teknik Sipil, Fakultas Teknik Sipil dan Pe-rencanaan (FTSP) Bidang Keahlian Manajemen dan Rekaya-sa Sumber Air, Institut Teknologi Sepuluh Nopember (ITS), Surabaya. Suryadi., 1986, Pengenalan Analisa Dengan Model Matematik Pada Masalah Air. Jurnal Penelitian dan Pengembangan Pengairan 2 : 3-6, Bandung. Spyros, M., Wheel Wright & Gee, M., 1999, Metode Peramalan, Bina Rupa Aksara, Jakarta. Sri Harto., 1999, Hidrologi Teori, Masalah dan Penyelesaian, Nafiri Offset, Jogyakarta. Triatmodjo, B., 1999, Teknik Pantai, Beta Offset, Jogyakarta.en_US
dc.identifier.issn1411-8904
dc.identifier.urihttp://hdl.handle.net/11617/1660
dc.description.abstractScientists have conducted many researches and developed models of salt intrusion due to tidal influence which collide with discharge of the river upstream. Most of developed models for forecasting the salt intrusion were physical or mathematical. Physical model are inflexible and very expensive when applied to salt intrusion cases, because every developed models only match with in the particular estuary on the other hand, mathematical models have a human limitation range when entering all of the variables to construct a model that represent the comprehensive natural phenomenon. In the last decade, the softcomputing model as branch of the artificial intelligence science were introduction as a forecast tool beside knowledge based system expert system, fuzzy logic, artificial neural network, and genetic algorithm. This reseach choose softcomputing model as an aid program in the system modeling because it has several advantages,which are operate in non linear system that can hardly be modeled mathematically, and have a parameter flexibility as an input of the model. Using all of the spesific advantages that the stated above, this research have a main purpose to develop the estuary salt intrusion forecasting model in dry season due to the influence of daily maximum tidal wave which collide with discharge of the river upstream using softcomputing approach. The method that used in this research was a combination between fuzzy logic and artificial neural network which usually called neuro fuzzy system of adaptive neuro fuzzy inference system algorithm (ANFIS). The main of research proved that the forecast result of the model that use M-SINFES as an aid program software were very sensitive to changes in range of influence parameter and were also have an accurate forecast range for a day ahead ( L ) when used a secondary data of measurement between 12 August – 7 October 1988 in estuary of Bengawan Solo. The configuration of the model can be described in a relationship pattern will be stated : L = ( Input : height of the daily maximum tidal wave (Ht), discharge of river upstream (Qt), and length of salt intrusion (L1+t1+tt) ; adaptive neuro fuzzy inference system : change in the value of range of influence parameter compared with amount of rules of fuzzy inference system, and mean square error of the learned and tested data ). The performances of the ANFIS model both training and testing data were evaluated and the best training/testing data selected according to stastical parameter such as mean square error (MSE) is 1.24 .10-6 (Scheme 7).en_US
dc.publisherlppmumsen_US
dc.subjectmodelen_US
dc.subjectforecastingen_US
dc.subjectsalt intrusionen_US
dc.subjectestuaryen_US
dc.subjectsoftcomputingen_US
dc.subjectadaptive neuro fuzzy inference systemen_US
dc.titlePRECISION RANGE IN SALT INTRUSION FORECASTING RESULT OF THE ESTUARY OF BENGAWAN SOLO USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM MODEL APPROACHen_US
dc.title.alternativeJANGKAUAN KETEPATAN HASIL PERAMALAN PANJANG INTRUSI AIR LAUT DI MUARA SUNGAI BENGAWAN SOLO MENGGUNAKAN PENDEKATAN MODEL ADAPTIVE NEURO FUZZY INFERENCE SYSTEMen_US
dc.typeArticleen_US


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