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:
Boarding houses are an inseparable part of students, mainly migrant students. In addition to resting, boarding can also be a place for study and discussion. Therefore, it needs reasonable consideration before choosing a boarding house. Choosing the right boarding house can increase student productivity. Currently looking for boarding houses you don't need to go around the campus, but you can use other boarding search applications. The application only displays data from the database directly without processing it first. When we open the application, the first display is boarding A. Then someone else opens the application the first to appear is boarding A again. Why not Boarding B? Even though the willingness of each user is different. Therefore, we need a recommendation algorithm to overcome these problems. In this research, the author uses the Collaborative Filtering algorithm. The dataset used comes from the Koseeker dataset from March 2020 - June 2020, totaling 1,897. The result of the calculation of Mean Absolute Error (MAE) is 0.4780. Applications made have an accuracy rate of 88.05%.
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.