SENTIMENT ANALYSIS OF THE NATIONAL COVID-19 VACCINATION PROGRAM ON TWITTER USING THE BIDIRECTIONAL ENCODER REPRESENTATION FROM TRANSFORMER

In Indonesia, the implementation of the national COVID-19 (Coronavirus disease of 2019) vaccination programmes has received criticism from various strata of society, especially through social media platforms such as Twitter. Therefore, Twitter can be used as a data source to analyze Indonesian public sentiment regarding the vaccination programme. Various classical machine learning methods exist for sentiment analysis, but these methods require complex feature engineering and do not focus on the importance of word order in a sentence. In this study, a deep learning model, bidirectional encoder representation from transformer (BERT), is used to overcome these problems by conducting experiments to determine the best dataset after pre-processing, the best hyper-parameter, and the best pre-trained model for BERT. The data used in this study were Indonesian Twitter data with a total of 3000 tweets. Our results demonstrate that BERT is suitable for performing sentiment analysis. In our experiments, BERT obtained better results than classical machine learning methods, with a precision of 86.2%, recall of 86%, f1-score of 86%, and accuracy of 86%. The results of the sentiment analysis performed in this study can be considered by the government in formulating policies related to the implementation of vaccination programmes. ICIC International ©2023.

VIRKOM as Augmented Reality Application for Visualization of Computer Maintenance Learning Material

Practical learning on computer maintenance for vocational students during the COVID-19 pandemic cannot be implemented optimally. Hardware access problems that are difficult to reach by students cause the learning barriers in this subject to be even greater. Therefore, through this research, a mobile-based learning media was developed to visualize computer component devices with AR technology and 3D animation. The research method used is the ADDIE model. The research begins by diagnosing the problem, describing needs, and finding appropriate solutions for computer maintenance learning. Next, the product design process and user journey are carried out and then develop applications with 3D animation assets and learning materials. Implementation activities go through an evaluation process to media and content experts to determine the validity of the application. The media and content validation instrument consists of 4 aspects with 46 items. This media is equipped with 3D objects that can be used to help students observe computer hardware. Media validation got a value of 79.49% and was included in the valid criteria so that it could be used in learning. Content validation is in the valid category with a value of 80.2%. Several improvements were made to increase the usability and attractiveness of the media so that students' interest in using the media increased. In the future this media can be applied in learning so that it can be seen the impact, both on learning outcomes, student interest and critical thinking on computer troubleshooting. © 2021 IEEE.