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Rapid technological advances make print communication media begin to be shifted by information media that utilizes computer technology as a means of delivery. Technological developments change the teacher's task from being a teacher to a facilitator who provides convenience in learning. SD Negeri 004 Bangsal Aceh is one of the schools that still applies the mathematics learning process using textbooks so that students do not understand the lesson. Teachers are required to make changes in teaching patterns in the classroom, one of which is with animated videos. This study uses the Multimedia Development Life Cycle (MDLC) method covering concept, design, collecting material, assembly, testing and distribution. The final result of this research is an animated video of mathematics learning about the material of flat shapes. Animated videos are used as an interesting and effective means of information and learning media. Besides that, it can also improve students' understanding and skills in learning mathematics, especially the material of flat shapes

Main Objective: This research targets AP sizing entrenched on image structure to raise denoising performance using an improved method for classifying image pixels. Background problem: Digital images may be blended by noise while the addition or communication process, affecting the authentic image signal. Image noise can cause problems at several stages of image processing equally image distribution. Accordingly, image denoising is a significant activity to recover the initial clean image signal from the detected noise signal. Novelty: The proposed WAV method has been refined and improved regarding the classification scheme, and the APS, and the classification results can be used as a mask on the noise image to fix identical patches. Research Method: This study proposes a WAV reprojection algorithm, with the PS being set dynamically entrenched on the image structure. Image structures are consistently taken with an upgraded and enhanced analysis method entrenched in the structure tensor matrix. Analysis results are also used to develop the analysis of comparable patches in images. Finding/Result: Empirical outcomes present that the noise cancellation work of the suggested method is better than the authentic WAVRA, along with several other modifications of the NLMA. Conclusion: In intensity profiles, the proposed method mostly has fewer changes to the original image values than other methods, thus, this method can be continued again to color images, and can also be applied to various types of data such as medical images.   Keywords: Weighted Average (WAV), Noise Cancellation Image, Non-Local Means (NLM), Adaptive Patch