Batik is a traditional clothwith unique patterns applied to fabric using a wax-resist dyeing technique. Aside from preserving this rich cultural heritage, the automated recognition of Batik patterns would enable many interesting applications. This paper introduces an approach to batik pattern recognition using the Scale Invariant Feature Transform (SIFT) as a feature extraction method. The challenging issues that arise are due to the highly symmetrical and repetitive properties of batik patterns. The Hough transform, as an evidence-based method of object detection, is applied to handle mismatched keypoints resulting from symmetrical and repetitive patterns of batik. On a collection of 120 batik images generated from 20 basic batik patterns, the proposed method showsan improvement over the original SIFT matching method with an equal error rate of 8.47%

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result, the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of epoch and batch_size is 100 and 20 respectively.

This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.

Internet merupakan kebutuhan yang penting digunakan dalam sebuah perusahaan. Koneksi internet yang digunakan dalam perusahaan harus stabil dan terhindar dari segala macam gangguan. Sebagian besar perusahaan sudah menggunakan dua link koneksi internet yang berguna untuk kelangsungan bisnis perusahaan. Penggunaan dua link koneksi tersebut tidak diimbangi dengan penerapan teknik load balancing dan failover. Pada penelitian ini masalah yang diangkat adalah penerapan load balancing yang sesuai dengan kebutuhan untuk mengoptimalkan kedua link koneksi internet. Metode load balancing yang digunakan adalah Equal Cost Multi Path (ECMP). Selain digunakan untuk membagi beban trafik pada kedua link, load balancing dengan metode ECMP juga dapat memberikan efek failover. Dalam penelitian ini implementasi diterapkan pada dua divisi yaitu divisi Customer Support Engineer (CSE) dan Customer Accounting Executive (CAE) yang terdapat di PT. Cyberindo Aditama. Hasil pengujian menunjukkan bahwa load balancing dengan metode ECMP dapat menggunakan link koneksi internet secara bergantian sehingga dapat mendistribusikan beban trafik secara merata pada kedua link sedangkan efek failover dihasilkan jika salah satu link terputus maka beban trafik akan secara otomatis berpindah ke link koneksi lain yang masih aktif. Berdasarkan pengamatan yang dilakukan saat terjadi load balancing diperoleh bahwa divisi CSE memiliki throughput 99.96% (sangat bagus), delay 1.70 ms (sangat bagus), dan jitter 0.02 ms (sangat bagus) sedangkan divisi CAE memiliki throughput 99.92% (sangat bagus), delay 1.69 ms (sangat bagus), dan jitter 0.01 ms (sangat bagus).

Batik is the fabric which is truly unique to Indonesia. Batik image retrieval is the research area which focuses on image processing and image retrieving based on its characteristics. This study investigated the performance of the feature selection and reduction on the batik retrieval process. The feature employed in this experiment is the combination of four feature extraction methods, which are Gabor filter, log-Gabor filter, GLCM, and LBP. SFFS methods is used to carry out the selection of features, meanwhile, PCA is used to perform the reduction feature. Based on the experiment, PCA can increase the precision about 17%. Meanwhile, SFFS can improve the execution time 1800 times faster.