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All Journal Jurnal Masyarakat Informatika Jurnal Simetris Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Soft Computing Exploration Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Sinaga, Daurat
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Scientific Journal of Informatics

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Multi-Layer Convolutional Neural Networks for Batik Image Classification Sinaga, Daurat; Jatmoko, Cahaya; Suprayogi, Suprayogi; Hedriyanto, Novi
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3309

Abstract

Purpose: The purpose of this study is to enhance the classification of batik motifs through the implementation of a novel approach utilizing Multi-Layer Convolutional Neural Networks (CNN). Batik, a traditional Indonesian textile art form, boasts intricate motifs reflecting rich cultural heritage. However, the diverse designs often pose challenges in accurate classification. Leveraging advancements in deep learning, this research proposes a methodological framework employing Multi-Layer CNN to improve classification accuracy. Methods: The methodology integrates Multi-Layer CNN architecture with an image dataset comprising various batik motifs, meticulously collected and preprocessed for uniformity. The CNN architecture incorporates convolutional layers of different sizes (3x3, 5x5, and 7x7) to extract unique features from batik images. Training options, including the Adam optimizer and validation frequency, are optimized based on parameters to enhance model efficiency and effectiveness. Result: Results from the experimentation demonstrate significant improvements in classification accuracy, with an overall accuracy rate of 90.88%. Notably, precision and recall scores for individual batik motifs, such as Motif Cual Bangka and Motif Rumah Adat Belitung, reached remarkable levels, showcasing the efficacy of the proposed approach. Novelty: This study contributes novelty through the integration of Multi-Layer CNN in batik classification, offering a robust and efficient method for identifying intricate batik motifs. Additionally, the research presents a pioneering application of deep learning techniques in preserving and promoting traditional cultural heritage, thereby bridging the gap between tradition and modern technology.
PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography Susanto, Ajib; Sinaga, Daurat; Mulyono, Ibnu Utomo Wahyu
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.9561

Abstract

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.
Co-Authors Abdussalam Abdussalam, Abdussalam Agustina, Feri Ajib Susanto Alzami, Farrikh Award Widya Laksana, Deddy Cahaya Jatmoko Candra Irawan Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Guruh Fajar Shidik Hadi, Heru Pramono Hedriyanto, Novi Heribertus Himawan Heru Lestiawan Lalang Erawan Marjuni, Aris Megantara, Rama Aria Mulyono, Ibnu Utomo Wahyu Nova Rijati Pergiwati, Dewi Pulung Nurtantio Andono Ricardus Anggi Pramunendar Saifullah, Zidan Sindhu Rakasiwi suprayogi Suprayogi Suprayogi Syamsiar, Syamsiar Yaacob, Noorayisahbe Mohd Yaacob, Noorayisahbe Mohd. Zainal Arifin Hasibuan