Penerapan Adaboost Berbasis Pohon Keputusan Guna Menentukan Pola Masuknya Calon Mahasiswa Baru

• Main Author: Nisa Hanum Harani
• Format: Article
• Language: Indonesian
• Published: Universitas Semarang 2020-08-01
• Series: Transformatika
• Subjects: higher education; decision tree; adaptive boosting
• Online Access: https://journals.usm.ac.id/index.php/transformatika/article/view/1606

In general, the college admission process is done through registration, file selection, examinations, an announcement of the results of students who pass, and ends with re-registration. In this case, a problem was found where there is a significant decrease in the number of student who register with those who re-register .Things like this can reduce the balance between new students and students who meet the requirements, to make a decrease in the quality of higher education and affect accreditation. Based on these problems, a classification method was developed to look for patterns of students who would enter institutions and what factors influence students to re-register. To improve the accuracy of the decision tree algorithm the author use adaptive boosting (adaboost) in finding factors that make prospective students continue to the re-registration process. From the results of the study, the AdaBoost-based decision tree algorithm shows that the level of accuracy has an increase of 20%. The presentation of results is as follows, 61.4% (decision tree); 91.35% (decision tree + AdaBoost)