Dampak Peningkatan Efisiensi Bank Syariah melalui Rancangan Model Enterprise Data Warehouse (Edw) untuk Kebutuhan Konversi Data Menjadi Format Xbrl

Bank Indonesia (BI) sebagai Bank Sentral mempunyai tugas untuk melakukan pengawasanterhadap Bank Komersil di Indonesia. Pengawasan BI tersebut melekat kepad a Bank KomersilKonvensional dan Syariah atau Unit Usaha Syariah. Sistem pelaporan ini diwajibkan oleh BankIndonesia dengan mengeluarkan ketentuan dan Surat Edaran (SE). Aplikasi Laporan ini sekarangsedang dilakukan enhancement menjadi LBUS Basel 2 dengan bentuk format data XBRL.Perubahan Sistem Pelaporan LBUS ini sangat signifikan sekali Perubahannya terutama Perubahankonten dan bisnis rule. Dari sisi teknikal ada Perubahan format data dari textfile menjadi formatXBRL. Bank Muamalat Indonesia (BMI) salah satu bank komersial Syariah harus melakukanpengembangan sistem pelaporan LBUS Basel 2 XBRL. BMI akan membangun Enterprise DataWarehouse dan Software untuk melakukan Konversi data menjadi format XBRL. Aplikasi tersebutdiharapkan akan dapat mengurangi biaya lisensi tahunan yang cukup besar. Aplikasi ini dirancangterintegrasi antara EDW dengan Mapper data untuk merubah konversi data menjadi Format XBRL,didalam satu lingkungan Extract Transformation Loading ETL, SSIS SQL Server 2012.Implementasi rancangan ini dapat meningkatkan efektif dan efisiensi terhadap pengurangan biaya,jumlah karyawan disetiap cabang tidak diperlukan sebab sudah tersentralisasi dikantor pusat. Dapatmengurangi masa pelaporan hari yang sangat signifikan. Mengurangi biaya lisensi dan biayahardware yang tinggi dari dampak solusi rancangan sistem konversi XBRL ini. Solusi ini dapatmenghemat 90% biaya pengembangan sistem dari nilai implementasi sistem lisensi

THE PREDICTING FACTORS OF MUSEUM VISITOR INTENTION: A STUDY OF MUSEUM WAYANG JAKARTA

Abstract       This study examines the influence between the product and the management of attraction on revisit intentions. The sample were taken by accidental sampling method of 150 respondents. Data were collected by survey with questioner method, field orientation, and interview. The data were analyzed using multivariate regression analysis to determine the influence between the product, the management of attraction and revisit intention. The product consist of location, accessibility, variety on site attractions, high quality environment, facilities, and price. The result showed that accessibility, variety on site attraction, and facilities has a significantly positive effect on revisit intention, meanwhile environment and price was not significantly influence on revisit intention. On the contrary, location has a negative effect on revisit intention. The management of attractions include the tangible elements of product, the characteristics of service delivery, and human resources management. The tangible elements of product and human resources was not significantly influence on revisit intention, but the characteristics of service delivery has a positive influence on revisit intention. The result indicate that the product has a dominant and significantly positive influence on revisit intention and the management of attractions also has a significantly positive effect on revisit intention . The product and management of attractions simultaneously has a significantly positive effect on revisit intention. The study provided a more through understanding the factors that may effect success of museum, which may help governments better understand the visitor needs.

Multiple feature-enhanced synthetic aperture radar imaging

Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the effectiveness of this approach on various SAR images

Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Nonquadratic regularization-based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data