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All Journal Elektron Jurnal Ilmiah SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Jurnal Rekayasa elektrika
Silfia Rifka
Politeknik Negeri Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Journal : Elektron Jurnal Ilmiah

 1 
RESTORASI CITRA MENGGUNAKAN JARINGAN SARAF TIRUAN (HOPFIELD) Silfia Rifka
Elektron : Jurnal Ilmiah Vol 1 No 2 (2009): Elektron Jurnal Ilmiah
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1739.394 KB) | DOI: 10.30630/eji.1.2.19

Abstract

The final goal of restoration is image improvement. In general, restoration to degradation modeling and implementation of invers process to get real image. Hopefield method in neural network restoration of image is done by parameters approximation in neural network modeling and reconstruction of degradation image to get real image. In this paper, this method is used image restoration which combines with Gaussian Blur, Uniform Blur and Gaussian Noise. The restoration process using this method take place really fast and suitable to defects restoration of recorder device which recognize the cause of defects and high process speed is needed.
Implementasi Pengolahan Citra Untuk Identifikasi Daun Tanaman Obat Menggunakan Levenberg-Marquardt Backpropagation Atsilfia Alfath Syam; Silfia Rifka; Siska Aulia
Elektron : Jurnal Ilmiah Volume 13 Nomor 1 Tahun 2021
Publisher : Teknik Elektro Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/eji.0.0.176

Abstract

Digital Image processing implementation can be applied to identify medicinal leaves, because it can help the elderly and people with color-blindness in identifying medicinal leave to be consumed and in avoiding reading errors, since some leaves have similar shape and color . In this discussion, the feature-extractions are using color and shape features, and using Levenberg-Marquardt for pattern recognition algorithm. The success of this medicinal plant identification system resulted in fairly good accuracy. The backpropagation network architecture used two hidden layers with 10 and 5 neurons. Data training is using 60 training leaf images with 15 images each of 5 types: green betel leaf, red betel, soursop, castor and aloe vera. Then, offline testing is using 20 test images for each of 4 images from 5 types with the accuracy of 85%. Meanwhile the online (realtime) test is using 20 times for each leaf types so the accuracy is 88%.
Co-Authors Amelia Yolanda Anak Agung Gede Sugianthara aprinal adila asril Atsilfia Alfath Syam dedi kurniadi Efrizon Efrizon Firdaus Firdaus Ikhram Hidayatullah Ramiati Ramiati Randy Heriyanto Ratna Dewi Rikki Vitria Rimra, Ihsan Lumasa Riza Widia Siska Aulia Uzma Septima - Waldito Febri Ramadhan Wiwik Wiharti Zas Ressy Aidha