Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
HAFIZHANA, YASQI
Unknown Affiliation
Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
IMAGE WATERMARKING PADA CITRA MEDIS MENGGUNAKAN COMPRESSIVE SENSING BERBASIS STATIONARY WAVELET TRANSFORM HAFIZHANA, YASQI; SAFITRI, IRMA; NOVAMIZANTI, LEDYA; IBRAHIM, NUR
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 1 (2020): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i1.43

Abstract

ABSTRAK Watermarking pada citra medis dilakukan untuk melindungi hak kepemilikan dan keaslian sebuah citra medis. Proses embedding dan extraction dirancang menggunakan metode Stationary Wavelet Transform (SWT) dan Statistical Mean Manipulation (SMM) untuk mengubah citra host menjadi sinyal sparse kemudian memasuki proses watermarking. Citra watermark dioptimasi dengan menggunakan metode Compressive Sensing (CS). Hasil akhir dari penelitian ini menunjukkan simulasi Image Watermarking dengan Bit Error Rate (BER) mendekati nilai nol dan PSNR lebih besar dari 40 dB, tanpa diberikan serangan. Penerapan Compressive Sensing menyebabkan nilai PSNR meningkat hingga 3,5 dB dan embedding capacity menjadi empat kali lipat lebih baik. Kata Kunci: Image watermarking, Telemedicine, Stationary Wavelet Transform, Statistical Mean Manipulation, Compressive Sensing. ABSTRACT Watermarking in medical images is carried out to protect ownership rights and authenticity of a medical image. The embedding and extraction process was designed using Stationary wavelet transform (SWT) and Statistical Mean Manipulation (SMM) methods to convert the host image into a sparse signal and then enter the watermarking process. The watermark image is optimized using the Compressive Sensing (CS) method. The final result of this final project shows the simulation of Image Watermarking with the Bit Error Rate (BER) approaching zero and PSNR greater than 40 dB, without being given an attack. The application of the Compressive Sensing pursuit will cause the PSNR increase up to 3.5 dB and embedding capacity four times better. Keywords: Image watermarking, Telemedicine, Stationary Wavelet Transform, Statistical Mean Manipulation, Compressive Sensing.
Co-Authors Irma Safitri Ledya Novamizanti Nur Ibrahim