Implementasi Metode Decision Tree Pada Tingkat Prestasi Belajar Siswa di SMK Swasta Anak Bangsa - Nurhayati Nurhayati; Saifullah Saifullah; Riki Winanjaya (BEES: Bulletin of Electrical and Electronics Engineering, 2021)
Data Mining is a series of processes to explore added value in the form of knowledge that has not been known manually from a data set. This grouping aims to determine the level of student success in the learning process that has been carried out. The approach used is quantitative. The subjects of this grouping are Class X (Ten) Academic Years 2018 to 2020. The data collection technique used is the learning outcome test. This grouping is done using Data Mining Rapid Miner 5.3 software, where the results will prove that the results of the evaluation of learning achievement are carried out by applying the C4.5 Algorithm. The results obtained are an accuracy value of 71.43%, meaning that the resulting rule is close to 100% correctness. Where the results of the Class Achieving precision label is 63.89% and the label Not Achieving is 92.31%. In accordance with these provisions, the results of manual calculations with Rapid Miner testing produce 11 models of rules or rules for Student