This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.

With the rapid development of the world, science and technology have progressed a lot, where all require computerized work, including technology in the health sector. Health problems are one of the social issues that must be carried out, with the cost of medical treatment and doctor consultation is very expensive. It is difficult to get information and treat the disease properly and then to prevent strokes. To see someone who is at risk of strokes, of course, requires a diagnosis from a doctor or an expert in stroke disease. But unfortunately, not all of the society can support themselves by doctors or experts seeing that many strokes sufferers have to help quickly. Based on this problem, an expert system needs to diagnose or make a decision based on expert sources in a stroke to be more effective, accurate, and efficient. The system design uses the Certainty Factor Method to measure the certainty of 20 stroke symptoms and the Fuzzy Logic as logical logic in processing nine patient's medical history. In this study's discussion, getting the expert system's F-measure (F1 score) was 0.857, which indicates a good performance expert system.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Pelatihan video media pembelajaran ini menggunakan aplikasi zoom dan powerpoint. Kegiatan ini untuk memberikan wawasan pembuatan video media pembelajaran untuk para guru SMK Negeri 2 Pangkalpinang dimana selama covid-19. Pelatihan ini bertujuan untuk guru dapat memudahkan proses belajar mengajar secara daring sehingga siswa-siswi mudah memahami mata pelajaran yang disampaikan oleh guru yang bersangkutan. Pelatihan secara praktikum langsung di Aula SMK Negeri 2 Pangkalpinang ini menghasilkan penggunaan powerpoint dengan powerpoint show dan video serta materi dengan rekaman zoom yang di konvert mp4. Pelatihan media pembelajaran ini diadakan selama dua hari berturut-turut dan diikuti oleh para guru dengan jumlah peserta sebanyak 86 orang.

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