Penerapan Data Mining untuk Prediksi Kelulusan Siswa SD Negeri Sukaresmi Kota Bogor Dengan Alogaritma C4.5 - Nurul Elvida; Adika May Sari (CV. Creative Tugu Pena, 2024)
Education is currently very important for enlightening the future generation and thus must be prioritized. This study at SDN Sukaresmi Kota Bogor aims to meet graduation requirements for students who must attend classes and be present at school. This research uses the C4.5 Algorithm method to form a decision tree, which requires grade and attendance data to assess student quality. Data mining collects and classifies data using RapidMiner tools for decision-making. The decision tree process transforms data into a tree model, which is then converted into rules and simplified. Applying data mining is crucial for facilitating data management, predicting graduation, identifying problems, and analyzing data. The objectives of this research are to predict students' eligibility for graduation, discuss the decision tree using the C4.5 Algorithm, and understand how students meet graduation requirements. This research is also a requirement for graduation from the Information Systems Bachelor's program at Universitas Bina Sarana Informatika. The results of this study obtained data showing 91.00% accuracy, with 1 student achieving Excellent graduation status, 32 students with Good status, 16 students with Fairly Good status, 5 students with Fair status, and 1 student with Satisfactory status.