Salah satu warisan budaya Indonesia yang diakui dunia adalah kain batik. Beragamnya motif batik di Indonesia membuat masyarakat awam sulit membedakan motif-motif yang ada. Penelitian ini menggunakan convolutional neural network (CNN) dalam melakukan klasifikasi multi-label citra motif batik. CNN merupakan salah satu algoritma deep learning pengembangan multi-layer perceptron (MLP) yang telah banyak digunakan dalam klasifikasi data, khususnya klasifikasi citra.  Hasil penelitian menunjukkan akurasi penggunaan arsitektur CNN dalam melakukan klasifikasi multi-label pada 15 motif batik mencapai 91.41% dengan penggunaan epoch 100.

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.

The ability to read and write Javanese scripts is one of the most important competencies for students to have in order to preserve the Javanese language as one of the Indonesian cultures. In this study, we developed a predictive model for 20 Javanese characters using the random forest algorithm as the basis for developing Javanese script learning media for students. In building the model, we used an extensive handwritten image dataset and experimented with several different preprocessing methods, including image conversion to black-and-white, cropping, resizing, thinning, and feature extraction using histogram of oriented gradients. From the experiment, it can be seen that the resulting random forest model is able to classify Javanese characters very accurately with accuracy, precision, and recall of 97.7%.

Manukan adalah salah satu desa di Kabupaten Bojonegoro yang terletak di Kecamatan Gayam, Bojonegoro, Jawa Timur. Minimnya penghasilan keluarga dari profesi petani menuntut masyarakat untuk lebih kreatif dalam meningkatkan pendapatan keluarga. Bojonegoro memiliki potensi batik Jonegoroan yang menarik dan banyak diminati. Namun, tidak banyak masyarakat Bojonegoro yang memiliki pengetahuan dan ketrampilan membatik. Sehingga, perlu dilakukan pemberdayaan Ibu-ibu dalam mengembangkan industri kreatif Batik Jonegoroan. Pemberdayaan ini dilakukan melalui empat tahapan, yaitu sosialisasi Batik Jonegoroan, pelatihan cara membatik, pelatihan pengembangan industri kreatif batik dan pemasaran hasil batik. Dalam pelatihan ini, peserta dilatih untuk memahami dan mempraktikkan proses membatik dengan empat teknis pewarnaan dan perbedaan antara batik cap dan batik tulis. Teknis pewarnaan yang dipelajari meliputi teknik coletan, semok, kelengan dan laseman. Hasil pelatihan berupa kain batik Jonegoroan dipamerkan dan dijual secara online di marketplace dan media sosial, sedangkan secara offline di bazar dan pameran batik. Pengetahuan dan ketrampilan ini dapat dijadikan bekal untuk membuka peluang usaha pengrajin batik oleh ibu-ibu desa Manukan sebagai salah satu upaya meningkatkan pendapatan keluarga.

Manual attendance recording throws away a lot of teaching and administration time from the university. Research on automatic attendance recording that has been done can be divided into biometrics and non-biometrics uses. Almost all methods require additional device that it is costly and inflexible for class changes. The proposed method solves the problems by utilizing the standard features of smartphones that are owned by all student, this method uses Wi-Fi direct for class broadcasting process and temporary Wi-Fi hotspot for verification process. The experimental results show that the proposed method produces the time needed for the initialization process is 14980 ms and the verification process is 3640 ms.