Pemanfaatan Algoritma Decision Tree ID3 Bagi Manajemen Bimbel Untuk Menentukan Faktor Kelulusan Pada Sekolah Kedinasan - Juwita Tetra Marani Aliyah Nazanah; Muhammad Ihsan Jambak (KLIK: Kajian Ilmiah Informatika dan Komputer, 2023)
At present official tutoring is an option for students to support their preparation for the next level of education, not only limited to guiding study, usually tutoring management prepares certain strategies so that students can pass the selection at the intended official service. Official schools in Indonesia are currently quite popular and in demand by many students, this is due to the advantages of official schools, namely the cost of education is relatively cheaper and even free, under the auspices of state institutions and have a greater opportunity to work immediately after graduation. This high interest causes high competition to enter service schools. One of the most popular services today is the STIS Statistics Polytechnic. The New Student Selection Selection (SPMB) at the STIS Statistics Polytechnic went through many stages. So that this is of interest to the tutoring management to find out what factors determine the passing of the selection. Ignorance of tutoring management in knowing the passing factor can lead to a lack of effective strategies and learning processes for tutoring students. Therefore, in this study the application of data mining science was carried out, namely classifying data from the 2022 STIS Statistics Polytechnic SPMB results using the ID3 decision tree algorithm which aims to find out the main factors that determine which students graduate. Then, the results of the research can be a support for tutoring management decisions in making strategies and future evaluations. So that tutoring students get the most appropriate and appropriate coaching strategy based on the results of this study. The dataset was analyzed by Data Mining using the ID3 Decision Tree Algorithm. Based on the research conducted on the data, the Kappa value is 1,000, the Accuracy is 100%, the Recall is 100,000%, the Classification Error is 0.00 and the Precision is 100%