ORCID Profile
0000-0002-0138-5315
Current Organisation
Universitas Gunadarma
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Publisher: IEEE
Date: 11-2018
Publisher: Institute of Advanced Engineering and Science
Date: 08-2019
DOI: 10.11591/IJECE.V9I4.PP3121-3129
Abstract: In digital television systems such as DVB-T, service provider has difficulties to observe the quality of picture reception in the viewers’ television. This is due to the unavailability of quality feedback sent from viewers’ devices to the service provider. Therefore, this research proposes link adaptation method in DVB-T system based on image quality measurement at recipient side, so that service provider may adjust the transmission power in real-time to improve the image quality. Quality metric used in this research is human perception- based no-reference image quality metric, which does not need the presence of the reference frame. The quality assessment is focused on the severeness of blocking artifact, which is the dominant artifacts in MPEG video. The numerical results have shown that power adaptation could maintain good picture quality as well as transmission power efficiency at the same time on the digital television transmission system. The proposed scheme is also suitable for other DVB system as well as various digital television system standards.
Publisher: IEEE
Date: 05-2009
Publisher: The Science and Information Organization
Date: 2019
Publisher: IEEE
Date: 2005
DOI: 10.1109/ICW.2005.79
Publisher: IEEE
Date: 06-2007
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 2005
DOI: 10.1109/ICW.2005.60
Publisher: Universitas Ahmad Dahlan
Date: 09-2016
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 11-2008
Publisher: Elsevier BV
Date: 08-2009
Publisher: Institute of Advanced Engineering and Science
Date: 02-2022
DOI: 10.11591/IJECE.V12I1.PP1018-1029
Abstract: This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2007
Publisher: Pushpa Publishing House
Date: 23-10-2017
DOI: 10.17654/EC017051177
Publisher: IEEE
Date: 03-11-2020
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 03-11-2020
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 2004
No related grants have been discovered for Tubagus Maulana Kusuma.