Feature Selection With the Random Forest Packages to Predict Student Performance - Slamet Wiyono; Dyah Apriliani; Taufiq Abidin; Dairoh Dairoh (Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya, 2019)
Each study program seeks to improve the quality of education and accreditation. One element that becomes the value of accreditation is students who graduate on time. The more active students, the more students will graduate on time. Thus, the head of the study program needs to make predictions of students who will be inactive in the next semester. To make predictions, we must determine what features are needed. This article is the result of feature selection research to predict the active status of students. The selection of features using seven features using the RandomForest package from R Studio. One feature as output is the active status of students and six features as input i.e; grade point (GP), grade point average (GPA), parent work, school majors, school category, and student hometown. The results of the selection of features show the strongest features to the weakest are; grade points (GP), grade point average (GPA), work of parents, majors of origin, schools of origin, and student hometown