Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) BAREKENG: Jurnal Ilmu Matematika dan Terapan
Carita, Sa'aadah Sajjana
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MODIFICATION OF POLLARD RHO ALGORITHM USING NEGATION MAPPING Carita, Sa'aadah Sajjana; Kabetta, Herman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422.864 KB) | DOI: 10.30598/barekengvol16iss4pp1159-1166

Abstract

El Gamal encryption was introduced in 1985 and is still commonly used today. Its hardness is based on a discrete logarithm problem defined over the finite abelian cyclic group group chosen in the original paper was but later it was proven that using the group of Elliptic Curve points could significantly reduce the key size required. The modified El Gamal encryption is dubbed its analog version. This analog encryption bases its hardness on Elliptic Curve Discrete Logarithm Problem (ECDLP). One of the fastest attacks in cracking ECDLP is the Pollard Rho algorithm, with the expected number of iterations where is the number of points in the curve. This paper proposes a modification of the Pollard Rho algorithm using a negation map. The experiment was done in El Gamal analog encryption of elliptic curve defined over the field with different values of small digit . The modification was expected to speed up the algorithm by times. The average of speed up in the experiment was 1.9 times.
Double Face Masks Detection Using Region-Based Convolutional Neural Network Carita, Sa'aadah Sajjana; Hadiprakoso, Raden Budiarto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.23902

Abstract

Because of the fast spread of coronavirus, the globe is facing a significant health disaster of COVID-19. The World Health Organization (WHO) released many suggestions to combat the spread of coronavirus. Wearing a face mask in public places and congested locations is one of the most effective preventive practices against COVID-19. However, according to recent research wearing double face masker even provide better protection than just one mask. Based on this finding, various public places require double masks to proceed more. It is pretty tricky to monitor individuals in crowded public places personally. Therefore, a deep learning model is suggested in this paper to automate recognizing persons who are not wearing double face masks. A faster region-based convolutional neural network model is developed using the picture augmentation approach and deep transfer learning to increase overall performance. We apply deep transfer learning by fine-tuning the low level pre-trained Visual Geometry Group (VGG) Face2 model. This study used the publicly accessible VGGFace2 dataset and the self-processed dataset. The findings in this study show that deep transfer learning and image augmentation can increase detection accuracy by up to 11%. Consequently, the created model achieves 93.48% accuracy and 93.19% F1 score on the validation dataset, demonstrating its excellent performance. The test results show the proposed model for further research by adding the predicted dataset and class.
Co-Authors Herman Kabetta Raden Budiarto Hadiprakoso