<p>Indonesia is one of the countries that adheres to a democratic system. In the course of a democratic system it is marked by periodic general elections. In 2019 Indonesia held a general election simultaneously to elect the President, DPR, DPRD and DPD. After the election, a lot of opinion arise within the community, including on social media twitter. One of the topics discussed was the results of the quick count of the presidential election. Therefore, a method that can be used to analyze sentiment from the quick count opinion is needed, that is naive Bayes method. The aims of this study are to find the best naive Bayes model and to classify sentiments. The result shows the best accuracy of 82.90% with α = 0.05. The classification obtained is 34.5% (471) positive tweets and 65.5% (895) negative tweets on the results of the quick count.</p><p><strong>Keywords :</strong> sentiment analysis, naive Bayes classifier, elections, quick count</p>
Tuberculosis (TB) is caused by bacteria (Mycobacterium tuberculosis) that most often affect the lungs so called Pulmonary Tuberculosis or PTB. Diagnosis using a chest radiograph image manually by doctor requires a long time, even difficult to detect PTB. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify PTB based on chest radiograph images. This study uses data from the National Library of Medicine, Maryland, USA in collaboration with Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China including 663 images entered into two classes, normal and PTB. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. The classification results of the models built were 99.19% for training data and 80.60% for validation data with 75 epochs, and accuracy in the test data was 84% which means that the model was able to qualify 84% of the test data into normal classes and PTB appropriately. 25 correctly classified as normal lungs, 5 incorrectly and 26 correctly classified as PTB and 5 incorrect.
Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.
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