Digital interface design plays a vital role in user interaction with software, especially through visual elements such as color and typography. This study was motivated by the need to understand how these visual elements affect user comfort while interacting with an application or website. The purpose of this study was to analyze user perceptions of the use of color and typography, and their impact on visual comfort. The method used was a descriptive quantitative approach by distributing online questionnaires to 20 respondents from various age groups. The collected data were analyzed descriptively by comparing preferences and comfort based on the age of the respondents. The results showed that the majority of respondents felt that color and typography greatly influenced the comfort of using digital interfaces. Dark background colors and sans-serif fonts with proportional sizes proved to be the most preferred choices because they provided reading comfort and reduced eye fatigue. A balanced combination of color and typography was also considered important to improve readability and the overall user experience. In conclusion, the selection and use of appropriate visual elements contribute greatly to creating a friendly and comfortable interface design for users of various age ranges.

Digital image processing plays an important role in extracting visual information from natural objects, including the morphological structure of leaves. One of the crucial techniques in this process is edge detection, which is used to emphasize object boundaries and support pattern analysis. This study aims to compare three edge detection methods, namely Sobel, Prewitt, and Canny in recognizing patterns in leaf images using the Python programming language and the OpenCV library. The methods used include quantitative experiments by implementing the three edge detection techniques on grayscale leaf images, followed by visual result analysis based on the criteria of edge sharpness, morphological detail, noise, and computational efficiency. The results show that the Canny method produces the most accurate and clean edge detection, with the ability to capture small details and reduce noise significantly. Sobel shows quite good performance in highlighting the main structure of the leaf, while Prewitt produces simpler and less precise results. Based on the evaluation results, the Canny method is considered the most effective for the purposes of digital leaf pattern classification and analysis. This study provides an important contribution in selecting the optimal edge detection method for computer vision applications in the field of digital botany..