LAOLI, ALNIS GUSTIN (2018) PEMODELAN RAINFALL-RUNOFF DENGAN SISTEM SYARAF TIRUAN MENGGUNAKAN PERANGKAT LUNAK MATLAB. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Hydrological analysis is necessary for flood control or revitalization efforts. The analysis is needed to determine the amount of plan debit or design discharge. One component in the hydrological cycle is rain runoff. Rainfall runoff components can be either run-offs or larger flows such as water flow in the river. Runoff is part of rainwater that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network backpropagation method and binary sigmoid activation function. Backpropagation is an inherited learning algorithm and is commonly used by perceptrons with multiple layers to change the weights associated with neurons in the hidden layer. Backpropagation algorithm uses error output to change its weight values in backward direction. The location of the review is the Ciujung River Basin (DAS), the data used are rainfall and debit data of Ciujung River from 2011-2017. Artificial neural networks are information processing systems with characteristics and performance that are close to biological nerves. . This artificial neural system method is useful only for real time not for flooding back up. Based on the results of the training and simulation model 1 obtained the value of R2: 2012 = 0.85102; 2013 = 0.78661; 2014 = 0.81188; 2015 = 0.77902; 2016 = 0.7279. In model 2 the value of R2 = 0.8724 is obtained. In model 3 obtained the value of R2: January = 0.96937; February = 0.92984; March = 0.90666; April = 0.92566; May = 0.9128; June = 0.87975; July = 0.85292; August = 0.95943; September = 0.88229; October = 0.90537; November = 0.93522; December = 0.9111. With Mean Squared Error (MSE) averaging close to 0. If the data used for training more, the artificial neural network will result in a larger R2 value. Keywords: artificial neural network; rainfall-runoff; backpropagation; matlab Analisa hidrologi sangat diperlukan untuk upaya pengendalian banjir atau revitalisasi. Analisa tersebut diperlukan untuk menentukan besarnya debit rencana atau debit desain. Salah satu komponen dalam siklus hidrologi adalah limpasan hujan. Komponen limpasan hujan dapat berupa run-off (aliran permukaan) ataupun aliran yang lebih besar seperti aliran air di sungai. Runoff merupakan bagian air hujan yang masuk dan mengalir dan masuk dalam badan sungai. Pemodelan rainfall-runoff pada penelitian ini menggunakan Jaringan Syaraf Tiruan metode backpropagation dan fungsi aktivasi sigmoid biner. Backpropagation merupakan algoritma pembelajaran yang terwarisi dan biasanya digunakan oleh perceptron dengan banyak lapisan untuk mengubah bobot-bobot yang terhubung dengan neuron-neuron yang ada pada lapisan tersembunyinya. Algoritma Backpropagation menggunakan error output untuk mengubah nilai-nilai bobotnya dalam arah mundur (backward). Lokasi tinjauan adalah Daerah Aliran Sungai (DAS) Ciujung, data yang digunakan adalah data curah hujan dan debit Sungai Ciujung dari tahun 2011-2017. Jaringan syaraf tiruan adalah sistem pemroses informasi dengan karakteristik dan performa yang mendekati syaraf biologis. . Metode sistem syaraf tiruan ini berguna hanya untuk real time bukan untuk banjir kala ulang. Berdasarkan hasil training dan simulasi model 1 didapatkan nilai R2 : 2012 = 0.85102; 2013 = 0.78661; 2014 = 0.81188; 2015 = 0.77902; 2016 = 0.7279.Pada model 2 didapat nilai R2 = 0.8724 . Pada model 3 didapat nilai R2 : Januari = 0.96937; Februari = 0.92984; Maret = 0.90666; April = 0.92566; Mei = 0.9128; Juni = 0.87975; Juli = 0.85292; Agustus = 0.95943; September = 0.88229; Oktober = 0.90537; November = 0.93522; Desember = 0.9111. Dengan nilai Mean Squared Error (MSE) rata-rata mendekati 0. Jika data yang digunakan untuk training lebih banyak, maka jaringan syaraf tiruan akan menghasilkan nilai R2 yang lebih besar. Kata Kunci: Jaringan syaraf tiruan; rainfall-runoff; backpropagation; matlab
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