The implementation of Covid-19 vaccination in Indonesia turned out to have various pro and contra opinions from the public. The discovery of disinformation and misinformation about vaccines spread through social media content affects a person's absorption of information so which leads to vaccine delays. When in fact, vaccination is one of the biggest and most effective contributions to preventing the Covid-19 pandemic. Astrazeneca is one of the vaccines provided by the Indonesian government. This vaccine used to be controversial amongst the public regarding its halalness and the safety of the vaccine because of the issue of the said vaccine containing swine trypsin. Nowadays Twitter has become a place for users to express their concerns and opinion regarding the Covid-19 vaccine. Data obtained from Twitter will be useful if it is analyzed, one of which is sentiment analysis. In this study, data collection was carried out using the snscrape library with a total of 3105 tweets obtained from the period May to June 31, 2021. The dataset that has been collected is then preprocessed to optimize the data. After passing the preprocessing stage, the data was labeled as tweet class using a lexicon-based dictionary which resulted in 1275 tweets with positive opinin labels and 1830 tweets labeled as negative opinion. The aim of this study is to examines the performance of Naïve Bayes and Support Vector Machine with adding the weighting method TF-IDF (Term Frequency – Inverse Document Frequency). The evaluation results show that the Support Vector Machine has a greater accuracy, precision, recall and f1-score of 87.27%, 90.41%, 77,34% and 83.37% compared to Naïve Bayes which has an accuracy, precision, recall and f1- of 76.81%, 72.40%, 70.70% and 71.52%.
The learning model used greatly affects the learning process in the Covid-19 pandemic era. The online learning that has been passed in this one year has caused boredom. The learning process is too monotonous, the teacher's intonation is less varied, and not easy to interact directly with friends and teachers. Therefore, to achieve an effective and maximum learning process, the researcher proposes using video-based learning and gamification methods to increase deeper understanding of the material, motivation in learning, and student involvement in the learning process through the Learning Management System (LMS). The material presented will be transformed into more interactive and interesting videos such as simple animated videos, tutorial videos, podcast videos, and others. This research aims to provide positive benefits for students to be more active in discussing and collaborating and enthusiastic in doing all learning activities. The test to measure the level of motivation and involvement can be carried out in three stages, namely with pre-test and post-test, T-test and analytical data from student access to the LMS according to the indicators involved in this study such as video completion, total video, total comments, total badges, and completion of the game level. This study result indicates a positive influence from the application of video-based learning and gamification methods on LMS to increase student motivation and engagement.
The number of batik motifs in Indonesia is not comparable to the knowledge possessed by the Indonesian people about batik motifs. The diversity of batik motifs can be a problem because classifying them can only be done by those who are familiar with batik in depth, both the pattern and the philosophy behind the motif, most of which are elderly people. To classify batik accurately and quickly is to use image classification technology. In this study, data were obtained from the previous researchers' GitHub repository, google images, and camera shots with a total dataset of 3,534 images. The data only focused on five batik motifs, namely Ceplok, Kawung, Parang, Megamendung, and Sidomukti. Before the batik motif is processed, preprocessing is carried out to obtain various quality data. Then the dataset was trained using the CNN model then the results were retrained using the VGG-16 and Xception Transfer Learning models. The researcher made several model scenarios, namely the CNN model without Transfer Learning and the model with Transfer Learning which took into account the effect of the learning rate values of 0.0004 and 0.0001. Therefore, the results of the CNN model without Transfer Learning (M0) obtained training accuracy results of 89.64%. While the results of the model with the best Transfer Learning is the M2 model (CNN + VGG-16, learning rate = 0.0001) with an accuracy of 91.23%, a loss of 24.48%, and the test results obtained an accuracy of 89%. Based on the results of the classification method, it can be concluded that the CNN model with Transfer Learning performs classification better in terms of accuracy and computation time than the CNN model.
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