This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning process were also identified. Support and training are needed for lecturers to overcome these challenges. These findings provide valuable insights for the development of problem-based teaching practices in the context of digital image processing.

This research tests the usability of the platform on new students of Informatics Engineering. Usability of is measured through three main variables: effectiveness, efficiency, and user satisfaction. Data was collected through pre-test and post-test involving 20 respondents using questionnaires and practical tasks. 20 respondents using questionnaires and practical tasks. The results of the study showed that the effectiveness and efficiency of the Scratch platform increased significantly from the pre-test to the post-test, reaching a level of effectiveness and efficiency, showing that the time required to complete programming tasks is reduced once users are familiar with the platform. User satisfaction also increased, with the majority of respondents feeling more comfortable and interested in using Scratch after testing. This research concludes that the Scratch platform is effective and efficient as a programming learning tool for new Informatics Engineering students.
 

This research implements the Convolutional Neural Network (CNN) method to classify the various types of cat breeds that are common in Indonesia. This research attempts to create an automatic system that can definitely and accurately classify and identify the types of cat breeds that exist in Indonesia using image processing techniques. The data used contains a total of 600 images with each folder containing 200 images. Using this CNN method produces a validation accuracy rate of 54% in the process of classifying cat breeds. Research shows that further developing the image processing process will increase the accuracy value of the resulting system.