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All Journal Sinergi Jurnal Teknologi Elektro Jurnal Rekayasa Sipil International Journal of Artificial Intelligence Research Prosiding Seminar Nasional Teknoka Forum Mekanika JURNAL KAJIAN TEKNIK SIPIL Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Inovasi Masyarakat Jurnal Teknik Sumber Daya Air

Sebayang, Ika Sari Damayanthi

Universitas Mercu Buana

Author-ID : 1530575

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation

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COASTAL VULNERABILITY INDEX ANALYSIS IN THE ANYER BEACH SERANG DISTRICT, BANTEN Mawardi Amin; Ika Sari Damayanthi Sebayang; Carolina Masriani Sitompul
SINERGI Vol 23, No 1 (2019)
Publisher : Universitas Mercu Buana

Anyer Beach is one of the famous tourist destinations. In addition to tourist destinations, the Anyer beach also has residential and industrial areas. In managing coastal areas, a study of vulnerability is needed due to threats from sea level rise, abrasion/erosion and also high waves that can damage infrastructure and cause losses. The research method is to collect data of hydro-oceanography, coastal vulnerability index calculates (Coastal Vulnerability Index). The coastal vulnerability index is a relative ranking method based on the index scale physical parameters such as geomorphology, shoreline change, elevation, sea level rise, mean tidal, wave height. On the results of the analysis of the criteria of vulnerability based on the parameters of geomorphology in the category of vulnerable with scores of 4, shoreline change in the category of vulnerable with a score of 4, the elevation in the category of extremely vulnerable with scores of 5, sea level rise into the medium category with a score of 3, mean tidal in the category less susceptible with a score of 2, the wave height is very vulnerable in the category with a score of 5. The variable that most influences the vulnerability of Anyer Beach is elevation and wave height.

OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD Ika Sari Damayanthi Sebayang; Agus Suroso; Alnis Gustin Laoli
SINERGI Vol 22, No 3 (2018)
Publisher : Universitas Mercu Buana

The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network, back propagation method and sigmoid binary activation function. This model is used to simulate single or long-term continuous events, water volume, making it very appropriate for urban areas. Back propagation is an inherited learning algorithm and is commonly used by perceptron with multiple layers to change the weights associated with neurons in the hidden layer. Back propagation algorithm uses output error to change the values of its weight in the 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. Based on training and simulation results, obtained R2 value: 2012 = 0,85102; 2013 = 0,78661; 2014 = 0,81188; 2015 = 0,77902; 2016 = 0,7279. on model 2 = 0,8724. On model 3 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 MSE (Mean Squared Error) of 0,0018479. The closer value of MSE to 0 and the value of R2 close to 1 then the better designed artificial neural network. If the data used for training more, the artificial neural network will produce a larger R2 value.

Co-Authors Agus Suroso Agus Suroso Akhmad Wahyu Dani Alnis Gustin Laoli Ansyori Fernanda Arief Kurniadi Arno Adi Kuntoro Budi Yanto Husodo Carolina Masriani Sitompul Fadli Sirait Fina Supegina Indratmo Soekarno Kurniadhi, Widjojo Laoli, Alnis Gustin M. Cahyono Mawardi Amin Muhammad Fahmia Oties T Tsarwan Pradana, Hapril Arief Raden Herdian Bayu AshShiddiq Reni Karno Kinasih Rika Rosi Rosanti Rofiq, Muhammad Ainur Said Attamimi Sinulingga, Jef Franklyn Sitompul, Carolina Masriani Syaefudin Wibowo Tiara Rosa Andina Triyanto Pangaribowo

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