Dalam era digital saat ini, guru dan dosen diharapkan mampu mengintegrasikan teknologi dalam proses pembelajaran. Pelatihan pengembangan media pembelajaran menggunakan Animaker diadakan untuk membantu mereka dalam menciptakan konten pembelajaran yang menarik dan interaktif. Pelatihan ini dilaksanakan tim dosen UDINUS Semarang yang melibatkan 125 peserta yang terdiri dari guru dan dosen dari PPMULTINDO. Tujuan pelatihan ini agar bisa meningkatkan literasi digital dan keterampilan teknis peserta dalam membuat bahan ajar yang interaktif. Hasil dari kegiatan ini, para guru dan dosen mampu menghasilkan bahan ajar yang lebih inovatif, sehingga dapat meningkatkan motivasi belajar siswa. Dengan demikian, pelatihan ini menjadi langkah penting dalam mendukung transformasi pendidikan di era digital.

Situasi pandemic COVID-19 membuat dunia pembelajaran perlu penyesuaian cepat salah satunya dalam pembuatan media pembelajaran, dimana guru dan deosen berperan dalam menyajikan metode-metode dengan pemanfaatan teknologi informasi salah satunya melalui media pembelajaran. Kegiatan pengabdian yang dilaksanakan secara daring melalui aplikasi Zoom ini menitikberatkan pada Pelatihan Membangun Visualisasi Data Menggunakan Google Data Studio berwawasan teknologi agar dimiliki dan dikuasai guru dan dosen sebagai tenaga pendidik pada Perkumpulan Profesi Multimedia dan Teknologi Informasi (PPMultindo). Pelatihan ini juga mengubah pola pikir bahwa membuat media pembelajaran adalah sesuatu yang rumit dan membosankan dikarenakan tidak banyaknya guru dan dosen yang berkompetensi di bidang teknologi informasi, menjadi mudah dan menyenangkan. Hasilnya para guru dan dosen mempunyai kemampuan dasar untuk membuat media pembelajaran dan mengembangkannya dengan visualisasi data menggunakan Google Data Studio dan siswa nantinya dapat secara interaktif untuk mengaksesnya dan dapat meningkatkan pemahaman materi yang diberikan.

Health is a very important thing in life. Therefore, to maintain health, we need adequate rest. Without adequate rest, the body will not be healthy and fit. In this study, a person's sleep disorder prediction will be made based on their lifestyle and work. The predictions made will classify sleep disorders that are absent, sleep apnea and insomnia from certain lifestyles and work. The methods used to make predictions are decision tree classifier, random forest classifier and naïve Bayes classifier. The test was carried out using a total of 375 data which was broken down into 70% training data and 30% testing data. The results obtained after testing with test data are by using the decision tree classifier algorithm to get an accuracy of 89.431%, using the random forest classifier algorithm to get an accuracy of 90.244% and by using the naïve Bayes classifier algorithm to get an accuracy of 86.992%.

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.