Rizal Khoirul Anam

Rizal Khoirul Anam

The proliferation of digital media necessitates robust methods for copyright protection and content authentication. This paper presents a comprehensive comparative study of digital image watermarking techniques implemented using the spatial domain (Least Significant Bit - LSB), the frequency domain (Discrete Fourier Transform - DFT), and a novel hybrid (LSB+DFT) approach. The core objective is to evaluate the trade-offs between imperceptibility (measured by Peak Signal-to-Noise Ratio - PSNR) and robustness (measured by Normalized Correlation - NC and Bit Error Rate - BER). We implemented these three techniques within a unified MATLAB-based experimental framework. The watermarked images were subjected to a battery of common image processing attacks, including JPEG compression, Gaussian noise, and salt-and-pepper noise, at varying intensities. Experimental results generated from standard image datasets (USC-SIPI) demonstrate that while LSB provides superior imperceptibility, it is extremely fragile. The DFT method offers significant robustness at the cost of visual quality. The proposed hybrid LSB+DFT technique, which leverages redundant embedding and a fallback extraction mechanism, is shown to provide the optimal balance, maintaining high visual fidelity while exhibiting superior resilience to all tested attacks.

Rizal Khoirul Anam

The proliferation of digital food applications necessitates robust methods for automated nutritional analysis and culinary guidance. This paper presents a comprehensive comparative evaluation of a decoupled, multimodal pipeline for food recognition. We evaluate a system integrating a specialized visual backbone (EfficientNet-B4) with a powerful generative large language model (Google's Gemini LLM). The core objective is to evaluate the trade-offs between visual classification accuracy, model efficiency, and the quality of generative output (nutritional data and recipes). We benchmark this pipeline against alternative vision backbones (VGG-16, ResNet-50, YOLOv8) and a lightweight LLM (Gemma). We introduce a formalization for "Semantic Error Propagation" (SEP) to analyze how classification inaccuracies from the visual module cascade into the generative output. Our analysis is grounded in a new Custom Chinese Food Dataset (CCFD) developed to address cultural bias in public datasets. Experimental results demonstrate that while EfficientNet-B4 (89.0\% Top-1 Acc.) provides the best balance of accuracy and efficiency, and Gemini (9.2/10 Factual Accuracy) provides superior generative quality, the system's overall utility is fundamentally bottlenecked by the visual front-end's perceptive accuracy. We conduct a detailed per-class analysis, identifying high semantic similarity as the most critical failure mode.

Rizal Khoirul Anam

The widespread adoption of large language models (LLMs) such as ChatGPT, Gemini, and DeepSeek has significantly changed how people approach tasks in education, professional work, and creative domains. This paper investigates how the structure and clarity of user prompts impact the effectiveness and productivity of LLM outputs. Using data from 243 survey respondents across various academic and occupational backgrounds, we analyze AI usage habits, prompting strategies, and user satisfaction. The results show that users who employ clear, structured, and context-aware prompts report higher task efficiency and better outcomes. These findings emphasize the essential role of prompt engineering in maximizing the value of generative AI and provide practical implications for its everyday use.