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 Boarding houses and rented houses are occupancies that college students need, not only for them but also for the public who doesn't have enough money, mostly renting if they choose to live independently. The difficulty of finding suitable boarding houses or rented houses is the most common problem, one of them is about taste. This problem needs to get solved because every person has a different taste. People's personalities and interests are not always the same, which makes every person have other criteria. With the application's help, we can easily choose the most suitable boarding houses or rented houses from our criteria. The application will be showing the data that already processed with our input to make us choose the boarding or rented houses close to our standards. For that, we need some recommendation algorithms, and one of them is Fuzzy Logic.
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%.