Purpose: The purpose of this study is to enhance the classification of batik motifs through the implementation of a novel approach utilizing Multi-Layer Convolutional Neural Networks (CNN). Batik, a traditional Indonesian textile art form, boasts intricate motifs reflecting rich cultural heritage. However, the diverse designs often pose challenges in accurate classification. Leveraging advancements in deep learning, this research proposes a methodological framework employing Multi-Layer CNN to improve classification accuracy.
Methods: The methodology integrates Multi-Layer CNN architecture with an image dataset comprising various batik motifs, meticulously collected and preprocessed for uniformity. The CNN architecture incorporates convolutional layers of different sizes (3x3, 5x5, and 7x7) to extract unique features from batik images. Training options, including the Adam optimizer and validation frequency, are optimized based on parameters to enhance model efficiency and effectiveness.
Result: Results from the experimentation demonstrate significant improvements in classification accuracy, with an overall accuracy rate of 90.88%. Notably, precision and recall scores for individual batik motifs, such as Motif Cual Bangka and Motif Rumah Adat Belitung, reached remarkable levels, showcasing the efficacy of the proposed approach.
Novelty: This study contributes novelty through the integration of Multi-Layer CNN in batik classification, offering a robust and efficient method for identifying intricate batik motifs. Additionally, the research presents a pioneering application of deep learning techniques in preserving and promoting traditional cultural heritage, thereby bridging the gap between tradition and modern technology.

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.

Conveying appropriate information to be understood quickly and accurately is very important in various areas of life, both academic and non-academic. A teacher or lecturer is a teacher whose job is to educate and provide instruction to students or students. Data visualization is one way that can be used to present data. The advantage of this method is the availability of statistical graphics which can enrich the display of information so that the results are more interactive for the audience. Google Data Studio is an application launched by Google that can be used to convert raw data into interesting and strategic information for users. That way, the information becomes more appropriate to understand quickly and accurately.

Buku, merupakan salah satu bentuk media pembelajaran yang digunakan untuk menyampaikan bahan pembelajaran kepada peserta didik. Profesi guru dan dosen mengharuskan untuk selalu menyusun dan memperbaharui bahan pembelajaran sesuai jenjang dan obyek pembelajarannya. Tugas pembuatan bahan pembelajaran tersebut pada era digital saat ini telah dimudahkan melalui pembuatan buku elektronik atau e-book. Berkembangnya e-book memungkinkan pendidik untuk berimprovisasi menyajikan bahan pembelajaran yang uptodate dan menarik. Penyusun e-book dapat memasukkan konten-konten grafis maupun tekstual yang lebih menarik berbiaya murah dengan teknik desain grafis. Namun demikian, tidak semua pendidik memiliki kemampuan dalam membuat e-book yang menarik. Kegiatan peningkatan ketrampilan teknik desain dan layout pembuatan buku elektronik bagi guru dan dosen ini bertujuan untuk menambah wawasan, pengetahuan, dan ketrampilan teknik desain dan layout pembuatan e-book, terutama bagi guru dan dosen yang belum memiliki pengalaman dalam bidang desain grafis. Pelatihan dilaksanakan secara daring dan diikuti oleh 137 peserta guru dan dosen dari sekolah dan perguruan tinggi. Hasil pelatihan menunjukkan bahwa 93% peserta menyatakan bertambah pengetahuan dan ketrampilan dalam hal teknik desain dan layout pembuatan e-book berdasarkan tugas yang diberikan dari pelatihan.

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.

Beberapa produk kopi yang cukup terkenal adalah Kopi Robusta dan Kopi Arabika. Memanfaatkan teknologi untuk membantu mengidentifikasi bagaimana perbedaan penampilan biji kopi menjadi salah satu isu yang perlu di uji coba, bahkan, pengidentifikasian menggunakan data citra juga dapat dilakukan dengan optimal. Proses pengidentifikasian tersebut dilakukan dengan cara memanfaatkan teknik pengolahan citra digital. Pada penelitian ini, telah digunakan Algoritma K-Nearest Neighbor (K-NN) dan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) untuk melakukan proses pengklasifikasian biji kopi Robusta dan Arabika. Digunakan dataset gambar sebanyak 194 gambar biji kopi yang merupakan gabungan dari 97 data citra dari biji kopi Robusta, dan 97 data citra dari biji kopi Arabika. Dari keseluruhan dataset tersebut, digunakan sebanyak 174 data sebagai data latih, dan 20 data sisanya sebagai data uji. Akurasi tertinggi yang dihasilkan dari eksperimen ini sebesar 95% dengan ketentuan jarak piksel = 1, nilai K = 1, dan besar sudur = 45o.