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%.

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.

Abstrak-Fluktuasi harga bahan pokok yang tidak terkendali dapat menyebabkan kerugian bagi konsumen maupun produsen. Salah satu langkah untuk mengatasi permasalahan tersebut yaitu dengan membuat prediksi harga yang akurat sehingga tindakan preventif dapat dilakukan untuk meminimalkan gejolak harga. Dalam studi ini, ARIMA digunakan untuk memprediksi harga bahan pokok nasional dalam jangka pendek. Data harga harian dari dua belas bahan pokok pada empat horizon prediksi (1 hingga 30 hari ke depan) digunakan untuk menguji kinerja ARIMA dalam memprediksi harga bahan pokok. Hasil eksperimen menujukkan bahwa model ARIMA yang dihasilkan mampu memprediksi harga dengan tingkat error ratarata sebesar 2.22%.Kata Kunci-ARIMA, Bahan Pokok, Prediksi, Peramalan Abstract-Uncontrolled price fluctuation of basic commodities can harm both consumers and producers. One way to overcome the problem is by making accurate price prediction so that preventive actions can be conducted to minimize the price fluctuation. In this study, ARIMA is used to make short-term price prediction of national basic commodities. Daily pricing data of twelve commodities in four prediction horizons (1 to 30 days ahead) is used to test the performance of ARIMA in predicting the commodity prices. The experimental results showed that the ARIMA model was able to predict the price quite accurately with an average error rate of 2.22%.

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.

This study aims to apply the convolutional neural network (CNN) to classify batik based on its manufacturing method, namely Batik Tulis which are hand drawn, Batik Cap where stamps are used to create the pattern, and Batik Printing which are printed using textile printing machine. We collected 40 images for each type of batik with a total of 120 images. To speed up and simplify the model building process, we implemented transfer learning with 3 basic CNN model architectures, namely ResNet, DenseNet, and VGG with batch normalization. We also experimented with building a new dataset by breaking each image down into 30 smaller images. Image augmentation was also used to prevent overfitting as well as to provide variations in the training data. The experimental results with 5-fold cross validation show that densenet169 gives the best results on the original dataset with an accuracy of 79.17% while vgg13_bn shows the best performance on the modified dataset with an accuracy of 87.61%. All models showed an increase in performance when using the modified dataset, except densenet169 which did not show a significant difference in performance.