ABSTRACT
Twitter is one of the social media that is widely used where Indonesia occupies the 6th largest Twitter user in the world. This research is a quantitative study on fine-grained sentiment analysis that extracts sentiment with the topic of the covid vaccine from Twitter with the aim of implementing the Support Vector Machine algorithm. The research flow uses the SEMMA method (Sample, Explore, Modify, Model, and Assess). The collection of data sets in the form of tweets crawled from Twitter by utilizing the Twitter API at the sample stage for further exploration of the attributes of the data set at the explore stage. The modify stage is text preprocessing so that the data set is more structured. After that is the model stage which applies the lexicon based method to assign sentiment classes to the data set. Data sets that have labels will be classified using the Naïve Bayes method and the Support Vector Machine. The final stage of the SEMMA method is to assess the method applied using confusion matrix and k-fold Cross Validation. The accuracy results from the Support Vector Machine method, the best parameter results using the CV Grid Search are the rbf kernel with C=100 and degree = 0.01 resulting in an accuracy of 85%. The accuracy of the implementation of the Support Vector Machine algorithm produces good scores for the Covid-19 vaccine topic, so that the algorithm can be applied to the classification of sentiment analysis on new data. © 2022 IEEE.
ABSTRACT
The Distance Education System (DES) is a learning system by using online application facilities through internet that rapidly adopted in the education sector. However, there has been many obstacles and problems that hinder learning activities. It has been implemented in all level of education in Indonesia even more during pandemic Covid-19. DES is one of the innovations that have been adopted in education sector, hence its kind of technology can influence the intention of learning. The purpose of this study is to measure an individual perception on behavioural intention to use of Distance Education Systems (DES) in the province of DKI Jakarta area. Researchers used a combination of the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) research models. The sampling technique used is purposive sampling, which only selects the students of Islamic Junior high school (Madrasah Aliyah Negeri/MAN) in the province of DKI Jakarta area. The data analysis method used is PLS-SEM with the product software of SmartPLS 3.3.3. The results of the study indicate that 8 out of 13 hypotheses are accepted. The variable that has the greatest influence on behavioural intention to use is compatibility mediated by perceived usefulness. The results found that three IDT variables (relative advantages, complexity, trialability) have a significant effect on the perceived usefulness variable, then lead to it has a significant relationship on behavioral intention to use. In addition, the two of IDT variable namely compatibility and trialability have significant effect on the perceived ease of use then it has a significant effect on behavioral intention to use. The study found that the behavioural intention to use of DES influenced by the innovation characteristics (IDT) and mediated by the characteristics of technology itself (TAM). © 2022 IEEE.