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All Journal Jurnal Masyarakat Informatika Techno.Com: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Journal of Computing Theories and Applications Scientific Journal of Informatics Journal of Future Artificial Intelligence and Technologies
Islam, Hussain Md Mehedul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Scientific Journal of Informatics

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Hiragana Character Classification Using Convolutional Neural Networks Methods based on Adam, SGD, and RMSProps Optimizer Mulyono, Ibnu Utomo Wahyu; Kusumawati, Yupie; Susanto, Ajib; Sari, Christy Atika; Islam, Hussain Md Mehedul; Doheir, Mohamed
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.v11i2.2313

Abstract

Purpose: Hiragana image classification poses a significant challenge within the realms of image processing and machine learning. Despite advances, achieving high accuracy in Hiragana character recognition remains elusive. In response, this research attempts to enhance recognition precision through the utilization of a Convolutional Neural Network (CNN). Specifically, the study explores the efficacy of three distinct optimizers like Adam, Stochastic Gradient Descent with Momentum (SGDM), and RMSProp in improving Hiragana character recognition accuracy. Methods: This research adopts a systematic approach to evaluate the performance of a Convolutional Neural Network (CNN) in the context of Hiragana character recognition. A meticulously prepared dataset is utilized for in-depth testing, ensuring robustness and reliability in the analysis. The study focuses on assessing the effectiveness of three prominent optimization methods: Stochastic Gradient Descent (SGD), RMSProp, and Adam. Result: The results of the model performance evaluation show that the highest accuracy was obtained from the RMSP optimizer with an F1-Score reaching 99.70%, while the highest overall accuracy was 99.87% with the Adam optimizer. The analysis is carried out by considering important metrics such as precision, recall, and F1-Score for each optimizer. Novelty: The performance results of the developed model are compared with previous studies, confirming the effectiveness of the proposed approach. Overall, this research makes an important contribution to Hiragana character recognition, by emphasizing the importance of choosing the right optimizer in improving the performance of image classification models.
Hybrid Quantum Representation and Hilbert Scrambling for Robust Image Watermarking Sari, Christy Atika; Abdussalam, Abdussalam; Rachmawanto, Eko Hari; Islam, Hussain Md Mehedul
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.10140

Abstract

Purpose: This work aims to apply Quantum Hilbert Scrambling to enhance the security and integrity of image watermarking without affecting visual quality degradation. Further conception of the surveyed methods could result in a very good solution to conventional methods of watermarking in solving some problems of digital image security and integrity with new concepts of quantum computing. Methods: The paper reviews Quantum Hilbert Scrambling, whose computational complexity is . The process involves encoding the image into a quantum state, permuting qubits by the Hilbert curve, and embedding a watermark using quantum gates. Result: The quantitative performance evaluation metrics, like Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), have shown high Peak Signal to Noise Ratio (PSNR) values from 56.13 dB to 57.87 dB and Structural Similarity Index (SSIM) from 0.9985 to 0.9990, correspondingly. This justifies the fact that the quality degradation is very slight and the fine details of the structure are well maintained. Novelty: The proposed method uniquely integrates quantum computing with traditional watermarking steps for a secure and effective approach in digital watermarking. Further development should focus on improving the quantum circuit regarding computation efficiency, extending the applicability of the method to a wide range of images, and various situations in watermarking, and finding hybrid approaches by combining quantum and classical approaches towards better performance and scalability.
Co-Authors Abdussalam Abdussalam, Abdussalam Adityawan, Harish Trio Ajib Susanto Akbar, Ismail Apnena, Riri Damayanti Ari Sudrajat Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Doheir, Mohamed Eko Hari Rachmawanto Farroq, Omar Gerinata Ginting Guruh Fajar Shidik Herowati, Wise Heru Lestiawan Kusumawati, Yupie Mulyono, Ibnu Utomo Wahyu Muslikh, Ahmad Rofiqul Nugroho, Sandy Pradnyatama, Mehta Pulung Nurtantio Andono Rachman, Rahadian Kristiyanto Sarker, Md Kamruzzaman Stefanus Santosa Trisnapradika, Gustina Alfa Wellia Shinta Sari