Optimasi Algoritma C4.5 Menggunakan Metode Forward Selection Dan Stratified Sampling Untuk Prediksi Kelayakan Mahasiswa Penerima Beasiswa - Bentar Candra P; Kusrini, Kusrini; Tonny Hidayat (KLIK: Kajian Ilmiah Informatika dan Komputer, 2024)
Every prospective student has the opportunity to get a scholarship within an educational institution, but it is often not on target so a more accurate data mining approach is needed. However, the C4.5 algorithm has a weakness in its level of accuracy when managing large amounts of data so it needs to be optimized. This research aims to optimize the C4.5 algorithm using stratified sampling and forward selection methods in determining the eligibility of scholarship recipients. The data came from prospective students at Anwar Medika University with a sample size of 263 records which were then processed using the RapidMinner application for the C4.5 algorithm without optimization and the C4.5 algorithm with optimization of the stratified sampling + forward selection method. The research results show a higher level of accuracy in the C4.5 algorithm with optimization using the stratified sampling + forward selection method, namely 81.75% compared to the accuracy level in the C4.5 algorithm without optimization, namely 80.23%. Thus, the conclusion of this research is that the C4.5 algorithm with optimization using stratified sampling and forward selection methods is more effective and can overcome the shortcomings of the C4.5 algorithm without optimization