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All Journal REKA ELKOMIKA Bulletin of Electrical Engineering and Informatics Jurnal Teknik Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JETT (Jurnal Elektro dan Telekomunikasi Terapan) JURNAL KAJIAN TEKNIK ELEKTRO Jurnal Tekno Insentif TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Indonesian Journal of Electrical Engineering and Computer Science REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat
LITA LIDYAWATI
Institut Teknologi Nasional (ITENAS) Bandung
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Digital watermarking image using three-level discrete wavelet transform under attacking noise Lita Lidyawati; Arsyad Ramadhan Darlis; Lucia Jambola; Lisa Kristiana; Rea Ramada Jayandanu
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3565

Abstract

The authentication, identification, and copyright protection can be obtained by constructing the digital image watermarking technique. Watermark robustness and imperceptibility account for the capability of the hidden watermark to survive the manipulation. The proposed paper is a robust algorithm for digital image watermarking with 3-level discrete wavelet transform (DWT) with some attacks method. The 3-level DWT method was used constants α=0.01 and 0.03 as a function of how depth the watermark inserts to the host image in the insertion and extraction process. The algorithm was evaluated using 8 bits per pixel (bpp) grayscale, 1024x1024 pixels for the host image, and 256x256 pixels for the watermark image. The method is also implemented some experimental with attacks such as gaussian, salt and pepper, blurring, and compression. The algorithm is relatively acceptable of good quality, achieves low-value mean squared error (MSE), high peak signals to noise ratio (PSNR), and structural similarity index metric (SSIM) value approach to 1. It is found that the highest image quality measurements by using α=0.03 with the attacking method of salt and pepper yield MSE=0.01, PSNR=45.6 dB and SSIM=0.95, respectively. 
Shallot disease classification system based on deep learning Lidyawati, Lita; Darlis, Arsyad Ramadhan; Munawaroh, Sofa Jauharotul
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8498

Abstract

Shallot is one of the important horticultural commodities for society and has high economic value. The problem with shallot cultivation is disease attacks on plants, one of which is Fusarium wilt. With the condition that the shallot commodity at the farmer level has a high failure rate, it is hoped that this research can assist farmers in providing information about shallot plants that have diseased plant characteristics using deep learning system convolutional neural network (CNN) method by utilizing leaf images on shallot plants. This research was conducted using the ResNet-18 architecture, with a total of 400 data in the dataset divided into 2 categories, namely healthy and diseased Fusarium wilt. The device used to carry out the classification process in this research is a Jetson Nano 2 GB. The ratio used to form a model from the dataset is 80-20 (80% training data and 20% validation data). The accuracy results for the classification of shallot plant diseases using real-time leaf images during the day have an average accuracy value of 68% on healthy plants and 62% on Fusarium wilt plants, while at night it has an average accuracy value of 53% on healthy plants and 47% on Fusarium wilt plants.
Co-Authors Adisty Hanny Asri Adisty Hanny Asri AGITA SYAIMI PUTRI UTAMI AKMAL, MULIA EZA Alfin Fernando Tamba ANITA PURNAMA SARI ARCHANDHIKA, HEGAR REFALDY Arif Fahrizal Arsyad Ramadhan Darlis ARSYAD RAMADHAN DARLIS BIMA BIMA, BIMA BUDI PRATAMA, BUDI DANI RUSIRAWAN DECY NATALIANA Deden Nur Rokhman DEWI, IRMA AMELIA DWI ARYANTA EDI YUSUF, EDI Fauziah, Dini FAZAR GUNTARA PRAJA GEMA ALFARISI DERI HEIFAT AHMAD ABDULLAH HUSAENI, DZIKRI FACHRI JAPE ATHAN BANGUN Kania Sawitri KRISTIANA, LISA Latif, Mochamad Fazri Abdul LUCIA JAMBOLA M.ICHWAN M.ICHWAN Muhammad Aluh Munawaroh, Sofa Jauharotul Narisha Kalya Mahsa NURUL WULANDARI PAULINE RAHMIATI, PAULINE pribadi, aditya arif RAISA MUKHTARETA Ramdhani, Teguh RATNA SUSANA Rea Ramada Jayandanu REGINA RIYANTIKA ROVAN NUGRAHA RUSTAMAJI RUSTAMAJI SABAT ANWARI Satria Ilma Romadoni Siswanto, Wahyu Dwi TUTUN JUHANA Yanuary, Tio Hanif Yuli Sunarti ZUL RAMADHAN