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All Journal Makara Journal of Technology
Mia Rizkinia, Mia
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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ALOS/PALSAR Image Processing Using Dinsar and Log Ratio for Flood Early Detection in Jakarta Based on Land Subsidences Sudiana, Dodi; Rizkinia, Mia
Makara Journal of Technology Vol. 15, No. 2
Publisher : UI Scholars Hub

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Abstract

Flood that occurred in Jakarta is not only influenced by rainfall, urban planning system and drainage alone, but also may be involved land subsidence (LS). LS is possible in because Jakarta stands on top of layers of sediments and the presence of ground water consumption in very large quantities. In this research, the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) data was processed to determine the level of LS in Jakarta area and its relation to flood potential area. Differential interferometry method (DInSAR) was performed on two PALSAR data with different acquisition years, i.e. 2007 and 2008, respectively. DInSAR processing generated images containing information that can be converted into LS. To find the elevation changing area, log ratio algorithm was applied to those images as the additional analysis. The log ratio image is superimposed on the DInSAR result and Jakarta inundation map of 2009, to acquire the relationship between LS and the flood and flood vulnerability map of Jakarta based on LS. It is found that lands on the flooded area of 10.57 cm on the average, with a minimum and maximum of 5.25 cm and 22.5 cm, respectively. The greater the value of LS, inundation area also tend to widen, except in a few areas that have special conditions, such as reservoirs, river flow solution, water pump system and sluices. Accuracy of DInSAR result image is quite high, with the difference of 0.03 cm (0.18%) to 0.55 cm (3.37%) as compared to those from GPS measurements. These results can be recommended to the local government of Jakarta to minimize the potential risk of flood, as well as the subject of city planning for the future.
Observation of Center Disaster Damage on Pariaman and Wasior Using Differential Sar Interferometry (Dinsar) Sudiana, Dodi; Rizkinia, Mia
Makara Journal of Technology Vol. 16, No. 2
Publisher : UI Scholars Hub

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This study focuses on disaster observations in Pariaman (West Sumatera) and Wasior (Papua) using remote sensing techniques (differential SAR interferometry). Differential interferometry (DInSAR) method was performed on two PALSAR data sets with different acquisition months, i.e. about a month after and before disaster, respectively. The center damage of Pariaman earthquake and Wasior flood can be determined by deriving Land Subsidence using DInSAR method.
Evaluation of Primal-Dual Splitting Algorithm for MRI Reconstruction Using Spatio-Temporal Structure Tensor and L1-2 Norm Rizkinia, Mia; Okuda, Masahiro
Makara Journal of Technology Vol. 23, No. 3
Publisher : UI Scholars Hub

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Magnetic resonance imaging (MRI) is an essential medical imaging technique which is widely used for medical research and diagnosis. Dynamic MRI provides the observed object visualization through time and results in a spatiotemporal signal. The image sequences often contain redundant information in both spatial and temporal domains. To utilize this characteristic, we propose a spatio-temporal reconstruction approach based on compressive sensing theory. We apply spatio-temporal structure tensor using nuclear norm, in addition to the wavelet sparsity regularization. The spatio-temporal structure tensor is a matrix that consists of gradient components of the MRI data w.r.t the spatial and temporal domains. For the wavelet sparsity, we use L1 – L2 instead of L1 norm. We propose the algorithm using primaldual splitting (PDS) approach to solve the convex optimization problem. In the experiment, we investigate the potential benefit of adding the two regularizations to the compressive sensing problem. The algorithm is compared with PDSbased algorithm using conventional regularizations, i.e., wavelet sparsity and total variation. Our proposed algorithm performs superior results in terms of reconstruction accuracy and visual quality.
Co-Authors Dodi Sudiana, Dodi Okuda, Masahiro