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The visibility of a university’s website on the search engine becomes an essential factor to reach a wider audience. One way to improve the visibility of a website is through Search Engine Optimization (SEO). University’s website development with SEO is inseparable from the data model because SEO supporting factors are parts of the consideration in the components and structure of the data model. This study aims to build a data model for a university website accompanied by SEO. The relational data model is used in this study based on the performance and maturity in defining schema-based design. This study was conducted through four sequential stages: literature review, planning, implementation, and evaluation. The resulting relational data model is one that has accommodated four supporting factors for SEO, namely Meta description, Meta keywords, URL structure, and image description. This study has succeeded in building a relational data model at the abstraction level of conceptual and logical.  In the conceptual data model, one entity and 11 attributes are formed. The logical data model was implemented in independent work environments using RelaX and operational requirements can be fulfilled by representing each table or relationship in the schema using relational algebra.

Covid-19 is a disease that has been declared a global pandemic since March 2020. One of the challenges in dealing with the current Covid-19 pandemic is the widespread doubts about the use of vaccines, even though vaccination is one of the most successful ways to deal with infectious disease outbreaks. Vaccine hesitancy can be observed, among others, from public sentiment or perception on social media, one of them is Twitter. The existence of social media can affect the absorption of information received by a person, in this case social media is also a medium for anti-vaccine propaganda which can result in a decrease in public confidence in the Covid-19 vaccine. This study aims to develop a classification model using the Support Vector Machine (SVM) method for sentiment analysis of Tweet related to the Covid-19 vaccine. Several previous studies have conducted sentiment analysis related to Covid-19, but this research specifically conducts sentiment analysis on the topic of the Covid-19 vaccine so that data preparation and model configuration will be different. This study also uses the Design Science Research Methodology (DSRM) for research as a whole before focusing on the use of the SVM method. The results of the study consist of an algorithm for creating data sets and a classification model for sentiment analysis that can be used to determine public perceptions of the issue of Covid-19 vaccination. This study also compares the use of unigram and bigram tokenization. Based on the results obtained, the average value of each aspect of the evaluation measurement is higher when the bigram tokenization is used. Although higher, the value obtained has an insignificant difference in the range of 0.6% - 0.7%. In the evaluation results using unigram and bigram tokenization, the highest scores for all aspects of measurement, namely accuracy, recall, f-measure, and precision were 84%.

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