Analisa Validitas Penentuan Predikat Kelulusan Berdasarkan Data Individu Mahasiswa di Institut Teknologi Dirgantara Adisutjipto (ITDA) - Dwi Nugraheny; Anton Setiawan Honggowibowo (PT FORMOSA CENDEKIA GLOBAL, 2022)
The world of education will never be separated from student data and graduation data. Student data is usually obtained at the time of registration of new students to an institution. Various individual data and parental data will be stored in the student's individual data archive. It is unfortunate if the data is not used and only as an archive. In this study, students will utilize individual data in the form of parental income data and data on student visits to the library. Which individual data will be associated with the student's cumulative achievement index to determine the validity of the student's graduation predicate. One method that can be used is the K-Nearest Neighbor method. The K-Nearest Neighbor method is a method for classifying objects based on the training data that is closest to the object. This study aims to analyze the validity of the student graduation predicate based on individual data such as parental income data and student library visit data using the K-Nearest Neighbor method. The case study was conducted by taking data from the Adisutjipto Aerospace Technology Institute