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PERANCANGAN SISTEM INFORMASI PREDIKSI PASANG SURUT BERBASIS ANDROID DI PUSAT RISET KELAUTAN DESIGNING ANDROID-BASED TIDAL PREDICTION INFORMATION SYSTEMS AT THE MARINE RESEARCH CENTER
Abstract
ABSTRAKPasang surut air laut merupakan parameter yang sangat penting bagi navigasi di pelabuhan, Pusat Riset Kelautan,Marine & Coastal Data Laboratory(MCDL) membuat prediksi pasang surutdi 39 pelabuhan seluruh Indonesia. Penelitian ini bertujuan menghasilkan sistem informasiprediksi pasang surut berbasis android dengan menggunakan bahasa pemograman Java, GoogleMaps Api sebagai basemapnya dan data prediksi pasang surut yang dihasilkan oleh MCDL.Informasi yang ditampilkan dalam bentuk grafik dan data tabel untuk 14 hari kedepan (2 minggu),dengan resolusi temporal per jam dengan pemuktahiran (update) informasi per 14 hari. Dalamaplikasi android prediksi pasang surut (pasut) terdiri dari 4 menu yaitu Pelabuhan, Glosarium,About, Disclaimer. Menu Pelabuhan menampilan informasi nama pelabuhan perikanan. Menu Glosarium berisikan informasi daftar istilah pasang surut. Menu About berisikan informasi pemilikaplikasi yang bertujuan untuk pengguna dapat berinteraksi dengan pemilih aplikasi. Menu Dis-claimer (penyanggahan) berisikan informasi bahwa informasi perkiraan pasut ini digunakansebagai rujukan (referensi) ketinggian muka/paras air laut di lokasi Pelabuhan Perikanan untukkepentingan operasi keluar dan masuknya kapal. Informasi tentang kedalaman batimetri dankondisi dasar perairan lokasi tidak disediakan pada aplikasi iniABSTRACTTide is a very important parameter for navigation in ports, the Center for Marine Research,Marine & Coastal Data Laboratory (MCDL) makes tide predictions in 39 ports throughout Indonesia.This study aims to produce an Android-based tidal prediction information system using the Javaprogramming language, Google Maps Api as its basemap and tidal prediction data generated byMCDL. Information displayed in graphical form and data tables for the next 14 days (2 weeks), withhourly temporal resolution with updated information per 14 days. In the tide prediction androidapplication (tides) consists of 4 menus namely Port, Glossary, About, Disclaimer. The Port menudisplays information on the name of the fishing port. The Glossary menu contains tidal term glossaryinformation. The About menu contains application owner information that aims for users to interactwith the application chooser. The Disclaimer Menu contains information that this tidal estimatedinformation is used as a reference (reference) for sea level / sea level at the Fishery Port location forthe purpose of operating in and out of ships. Information about the bathymetry depth and thebottom waters of the location is not provided in this application.
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Tidal range in the Arafura and Teimor Sea region is estimated from the actual field records collected by five tidal stations during March 2011. These stations include Rote and Saumlaki tidal stations of Badan Koordinasi Survei dan Pemetaan Nasional (Bakosurtanal) Indonesia, and Broome, Darwin and Groote Eylandt tidal stations of Australia Bureau of Meteorology (BoM). In addition to data from these stations, datasets of sea surface height obtained from Topex/Poseidon altimetry at seven (7) virtual stations were used. Generally, the results of this study are in agreement with that of Wyrtki (1961). However, by utilizing spectral analysis and form factor, this study shows difference in terms of tidal types from that of Wyrtki's, particularly at Karumba and Groote Eylandt stations.
Abstrak Data pasut air (pasut) laut sangat diperlukan dalam penentuan garis pantai dan pelaksanaan survey bathimetri. Paper ini akan membandingkan hasil pengamatan pasut di lapangan dengan suatu prediksi yang dihasilkan dari perangkat lunak Tide Model Driver (TMD). Hasil yang diperoleh menunjukkan bahwa untuk wilayah perairan kepulauan, tipe pasut dari data pengamatan lapangan berbeda dengan tipe pasut yang diperoleh dari prediksi TMD. Sedangkan untuk wilayah perairan terbuka, tipe pasut dari data pengamatan lapangan sama dengan tipe pasut yang diperoleh dari prediksi TMD. Abstract Tidal data for sea water level is needed to determine of the coastline and the bathymetric survey. This paper will compare the results of tidal observations in the field with a prediction generated from the Tide Model Driver (TMD) software. The results showed that for the islands waters, tipe of tidal data from the field observation is different with the tipe of tidal predictions obtained from TMD. As for the open sea water, tidal data from the field observation match with the tipe of tidal predictions obtained from TMD.
A computationally efficient relocatable system for generalized inverse (GI) modeling of barotropic ocean tides is described. The GI penalty functional is minimized using a representer method, which requires repeated solution of the forward and adjoint linearized shallow water equations (SWEs). To make representer computations efficient, the SWEs are solved in the frequency domain by factoring the coefficient matrix for a finite-difference discretization of the second-order wave equation in elevation. Once this matrix is factored representers can be calculated rapidly. By retaining the first-order SWE system (defined in terms of both elevations and currents) in the definition of the discretized GI penalty functional, complete generality in the choice of dynamical error covariances is retained. This allows rational assumptions about errors in the SWE, with soft momentum balance constraints (e. g., to account for inaccurate parameterization of dissipation), but holds mass conservation constraints. While the dynamical calculations involve elevations alone, depth-averaged currents can be directly assimilated into the tidal model with this approach. The efficient representer calculation forms the basis for the Oregon State University (OSU) Tidal Inversion Software (OTIS). OTIS includes software for generating grids, prior model covariances, and boundary conditions; for time stepping the nonlinear shallow water equations to generate a first guess or prior solution; for preliminary processing of TOPEX/Poseidon altimeter data; for solution of the GI problem; and for computation of posterior error bars. Approximate GI solution methods, based on using a reduced set of representers, allow very large datasets to be inverted. OTIS regional and local GI tidal modeling (with grids containing up to 10(5) nodes) require only a few hours on a common desktop workstation. Use of OTIS is illustrated by developing a new regional-scale (1/68) model of tides in the Indonesian Seas.