Penerapan Metode K-Nearest Neighbor (KNN) dan Metode Weighted Product (WP) Dalam Penerimaan Calon Guru Dan Karyawan Tata Usaha Baru Berwawasan Teknologi (Studi Kasus : Sekolah Menengah Kejuruan Muhammadiyah 2 Kediri) - Nihru Nafi' Dzikrulloh; Indriati Indriati; Budi Darma Setiawan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2017)
World of particular employment agencies Vocational High School, many a teacher or school employee who less clever in technology of the current technological developments. Actually, it is in need of teachers and school administration employees who have qualified human resources high in the knowledge of science and technology. The school is in need it is because it affects how do learning on students in school. To meet the desired standards of quality teachers, during The Vocational High School Muhammadiyah 2 Kediri is selection and recruitment of teachers by means of manual employees. The selection has been done manually through the test phase 4 aspects of your application letter and attachments GPA averages, academic test, test general knowledge of science and technology (IPTEK), and interview. The data collection process for the selection still use manual. Therefore, we need a web-based system so that the selection acceptance of new teacher candidates can run more effectively and efficiently. On this website using K-Nearest Neighbor (KNN) and the method of Weighted Product (WP). K-Nearest Neighbor used to determine the weight of each criterion to classify the good or bad. After classifying the KNN method, the selection of prospective teachers will be recruited by the school Vocational High School Muhammadiyah 2 Kediri using Weight Product (WP). Weight Product used to determine the results of the classification by KNN method to perform a ranking in order to take the best results. Tests conducted consisting of, testing the accuracy of the value of K means and accuracy testing of the WP value criteria weighting method. The accuracy of the test results obtained suitability accuracy value by 94%, precision 80%, and recall 80%.