Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

This research aims to recognize the pattern of pulmonary disease on x-ray radiography image using artificial neural network (ANN) method. The images, which were used such as images of healthy pulmonary, pulmonary tuberculosis, and pulmonary tumour. Pattern recognition was using an extraction feature of GLCM (Gray Level Co-occurrence Matrix) and back propagation method. Before being identified, the images were processed by median filter and adaptive histogram equalization. The GLCM features that used were homogeneity, energy, contrast, variance and correlation. The parameters were learning rate and hidden layer. Learning rate was 0.3 and hidden layer was 25. Back propagation training showed 100% accuracy, which all of 44 images were used had been successfully identified. From the result, the healthy pulmonary showed 60% accuracy, 83.3% for pulmonary tuberculosis and 100% for pulmonary tumor. Hence, the overall result showed 81.25% accuracy, which 13 of 16 images had been successfully identified. From these result, extraction feature of GLCM using back propagation method was capable to recognize the pattern of pulmonary disease. However, due to narrow range of the feature, this application may not be used optimally for comparing features in every category of images. Therefore, the further research is needed to determine the best features and parameters of training back propagation.

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