Motion detection is an important process in computer vision to analyze activities in videos. This study implements a simple system to detect motion in video files using Python and the OpenCV library. The system works by comparing consecutive frames in a video to detect changes and mark areas that experience motion. The implementation shows satisfactory results on various sample videos. This study provides a solution that is easy to implement and can be used in applications such as video analysis and computer-based monitoring.

Crime is a growing social problem that needs to be addressed seriously. One way to prevent crime is through religious education. Religious education plays an important role in shaping personal character based on moral and ethical values, thus preventing deviant behavior early on. This study aims to explore an effective religious education model in instilling moral and spiritual values as a preventive effort against criminal behavior. The research method used is a qualitative approach that combines documentary analysis and case studies of religious education institutions in Johorejo Village, Gemuh District, Kendal Regency. The findings show that structured and consistent religious education, which internalizes values such as honesty, responsibility, and tolerance, is very helpful in reducing the tendency of criminal behavior. In addition, the active participation of families and communities in the religious education process is an important factor in strengthening these values. Therefore, the development of a comprehensive and sustainable religious education model is an effective strategy to prevent social crime..

Hijaiyah letters are the alphabet used in Arabic and the Koran. Automatic recognition of hijaiyah letters has many benefits, especially in the fields of education and learning Arabic and the Koran. This research aims to classify hijaiyah letter recognition using image processing techniques and the Support Vector Machine (SVM) algorithm. We collected a dataset of images of 5 hijaiyah letters with a total of 400 images obtained from Google and also Iqro'.  The train:test ratio is 8:2. Experimental results show that the proposed approach can achieve high accuracy in recognizing hijaiyah letters with an accuracy rate of 99.16%.