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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Majalah Ilmiah Matematika Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Agroekoteknologi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer InComTech: Jurnal Telekomunikasi dan Komputer J I M P - Jurnal Informatika Merdeka Pasuruan International Journal Multidisciplinary Science
Tubagus Maulana Kusuma
Gunadarma University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : International Journal of Electrical and Computer Engineering

 1 
Adaptive power link adaptation on DVB-T system based on picture quality feedback Tubagus Maulana Kusuma; Randy Rahmanto; Emy Haryatmi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (804.114 KB) | 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.
Automated hierarchical classification of scanned documents using convolutional neural network and regular expression Rifiana Arief; Achmad Benny Mutiara; Tubagus Maulana Kusuma; Hustinawaty Hustinawaty
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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.
Co-Authors Achmad Benny Mutiara Anisa Lora Ariska, Ardhi Muda Azmi, Tubagus Kiki Kawakibi Brizky Ramadhani Ismanto Busono Soerowirdjo Dina Anggraini Emy Haryatmi Fitriani, Ayu Hans-Jurgen Zepernick Hariyanto Hariyanto Hustinawaty Hustinawaty, Hustinawaty Khairunnisa Fadhilla Ramdhania Kurniasih, Ratih Muhammad Guruh Ajinugroho Musa, Purnawarman PARAGYA, DHATU Paulus Sakti Laksono Permadi, Yogi Prayogo, Sandy Suryo Rakhmi Khalida Randy Rahmanto Rifiana Arief Sarifuddin Madenda Sunny Arief Sudiro Suryarini Widodo