Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
This study will classify sentiment and analysis of general topics against President Joko Widodo taken through social media Twitter. The influence and benefits of sentiment are so significant that the study of analytical sentiment is increasing. This research has several stages, including data collection, data cleaning, data extraction, data classification using the Support Vector Machine method, and topic analysis using the Latent Dirichlet Allocation method. This study's results took data on Twitter taken from September 23, 2018, to December 15, 2020, as many as 1200 pieces of data in the form of 600 negative data and 600 positive data. In conclusion, the topic analysis system and sentiment classification performed the classification results using the Support Vector Machine method in this research, which obtained 86.39% accuracy by 85.75%, recall by 85.86%, and F1 score 85.79%—as for topic analysis using Latent Dirichlet Allocation got a perplexity value of 7.1049 on positive sentiment and 7.3165 on negative sentiment.
Twitter is social media that can be used to exchange ideas and give opinions. Twitter users can write their opinions on the issue of President Joko Widodo's government. Tweet data or public opinion can be done sentiment analysis method to analyze public opinion. The Naïve Bayes method is used to classify Twitter data to determine sentiment and grouping into positive class and negative class. Furthermore, topic modeling is carried out with the Latent Dirichlet Allocation (LDA) method to determine the topic of discussion in each sentiment group. In the classification process, the value of accuracy depends on the preprocessing stage and relies on the data amount. In train data 80% and test data 20% obtained accuracy 84.58%, recall 85%, precision 85% and F1-Score 85%. At the LDA stage, performance testing with perplexity resulted in a perplexity value of 7.1049 based on the number of iterations of 30 for the positive sentiment group. Furthermore, the perplexity value is 7.3165, with the number of iterations is 60 for the negative sentiment group.