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This paper describes a novel method for extracting features of batik images. This method is called enhanced microstructure descriptor (EMSD). EMSD is the enhanced model of micro-structure descriptor (MSD) which proposed by Guang-Hai Liu. Different with MSD that uses only edge orientation similarity for creating micro-structure map and then utilises this map along with color values; EMSD adds a new micro-structure map that is based on color similarity and then utilises this map along with edge orientation values. The combination of MSD and the additional micro-structure descriptor is used as feature extractor in EMSD. This method is tested on 300 batik images, Corel datasets with 5,000 images and 10,000 images. We also compared EMSD to MSD and multi-textons histogram (MTH), which EMSD performance is superior than the other two.

Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

Abstract

Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to seek the best classification method for Batik classification. The performance is tested 300 images divided into 50 classes. The results show the optimum accuracy achieved using k-NN with k=5 and MTH with 6 textons is 82%; however, SVM and MTH with 6 textons denote 76%. According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification.