Prediksi Kelulusan Siswa Sekolah Menengah Pertama Menggunakan Machine Learning - Naibaho, Agusti Frananda Alfonsus; Zahra, Amalia (Brahmana : Jurnal Penerapan Kecerdasan Buatan, 2023)
In recent years, there have been students who have not graduated on time at Lubuk Alung 1 Public Junior High School. This statement is supported by graduation data from SMP Negeri 1 Lubuk Alung. Therefore it is necessary to predict student graduation status to identify factors that influence student graduation, which can also be used to help schools solve problems more easily. To overcome this problem, researchers predict student graduation based on student graduation information. The attributes used are personal data related to students, student academic data, and data related to the work of students' parents. Researchers obtained data on student graduation from schools that had been recapitulated. The classification algorithms used are decision tree, random forest, and extreme gradient boosting with grid searchCV and k-fold=5. Predictive accuracy using the random forest algorithm outperforms other methods with a value of 99.5%.