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ITS » Master Theses » Manajemen & Rekayasa Sumber Air S2
Posted by dee@its.ac.id at 15/11/2014 00:06:25  •  911 Views


PEMODELAN ARTIFICIAL NEURAL NETWORK ANN UNTUK SISTEM OPERASIONAL BANGUNAN PENGATUR DI KALI SURABAYA

PEMODELAN ARTIFICIAL NEURAL NETWORK ANN UNTUK SISTEM OPERASIONAL BANGUNAN PENGATUR DI KALI SURABAYA

Author :
MAYASARI, HAKIKI ( 3111205004 )




ABSTRAK

Salah satu penyebab banjir di Surabaya adalah akibat hujan lokal karena lahan terbuka hijau di Surabaya semakin sedikit maka air akibat hujan menjadi aliran air permukaan yang banyak mengalir ke sungai. Salah satu cara mengurangi dampaknya adalah dengan mengoptimalkan bangunan pengatur yang ada di kali Surabaya dengan melakukan pemodelan dan prediksi debit dengan menggunakan Artificial Neural Network ANN Pemodelan dilakukan pada pintu air Gunungsari pintu air Wonokromo pintu air Jagir dan pintu air Gubeng. Penelitian ini menggunakan data debit pintu air Gunungsari pintu air Jagir pintu air Wonokromo pintu air Gubeng intake Jeblokan dan Kalibokor dari tahun 2006 sampai dengan tahun 2009 dan data debit per jam pada tahun 2012 sebagai variabel input. Metode yang digunakan adalah Levenberg-Marquardt backpropagation yaitu pembelajaran otomatis yang berhenti saat keseluruhan model mengalami peningkatan kali indikasinya adalah peningkatan dari nilai MSE pada data validasi. Hasil penelitian menunjukkna model terbaik dari masing-masing pemodelan adalah pintu air Jagir dengan arsitektur model 2-18-1 pintu air Gubeng dengan arsitektur model 5-12-1 dan pintu air Gunungsari dengan arsitektur model 3-15-1 kemudian dicari prediksi debit dari masing-masing pemodel pada masing-masing pintu air.


ABSTRACT

Local rain is one of the flood caused in Surabaya reduction of green area in Surabaya made water from the rain becomes a surface flow which directly flowing to the river. One of the solutions to minimize it is by optimizing the operational of the dams on rivers in Surabaya by models and forecast the discharge in pintu air using Artificial Neural Network ANN. In this case Gunungsari sluice gate Jagir sluice gate and Gubeng sluice gate has been used. The variable input for this research based on Gunungsari Jagir Wonokromo and Gubeng sluice gate also Jeblokan and Kalibokor intake inflowdischarge data from 2006 to 2009 and inflow every hours data in 2012. Levenberg-Marquardt backpropagation is the method used in this research when the whole model system increase to some limits it is tool is automatically stopped. The increasing of MSE data validationis used as the indicator. The result of this research shows that the best result for the modeling is Jagir sluice gate with the architectural model 2-18-1 Gubeng sluice gate with 5- 12-1 architecture models and the Gunungsari sluice gate with 3-15-1 architecture models. From the best architecture forecasting of the discharge on each sluice gate has been figured out the discharge forecasting results have the same trend with discharge recording field .



KeywordsOperasional; ANN; Prediksi
 
Subject:  Perangkat lunak komputer ; Jaringan syaraf tiruan
Contributor
  1. DR. techn. Pujo Aji, ST. MT
  2. Ir. Anggrahini, M. Sc
Date Create: 23/01/2014
Type: Text
Format: pdf
Language: Indonesian
Identifier: ITS-Master-31103140000932
Collection ID: 31103140000932
Call Number: RTS 006.32 May p


Source
Maste Thesis of Civil Engineering, RTS 006.32 May p, 2014

Coverage
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Rights
Copyright @2014 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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  1.  ITS-Master-33705-3111205004-abstract_id.pdf - 189 KB
  2.  ITS-Master-33705-3111205004-abstract_en.pdf - 184 KB
  3.  ITS-Master-33705-3111205004-conclusion.pdf - 200 KB




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