ITAF Kupang New Student Admission Prediction Using The Random Forest Method
DOI:
https://doi.org/10.62671/perfect.v3i2.284Keywords:
New Student Prediction, Random Forest, Data Mining, Student Admission, ITAF KupangAbstract
New student admission is a crucial aspect of higher education academic planning. The Alberth Foenay Institute of Technology (ITAF) Kupang requires a data-driven approach to predict the number of new students in each study program to support more accurate decision-making. This study aims to predict the number of new student admissions at ITAF Kupang in the 2026/2027 academic year using the Random Forest method. The data used comes from historical data on new student admissions over the past five years (2021–2025) in three study programs: Informatics, Environmental Engineering, and Mechanical Engineering. The year and study program variables are used as input variables, while the number of new students is used as the output variable. The research stages include data pre-processing, transformation and encoding of categorical variables, Random Forest modeling, and model evaluation using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The model evaluation results show an MAE value of 9.11 and an RMSE of 10.58, indicating that the model has quite good predictive performance. The prediction results show that the number of new students in the 2026/2027 academic year is estimated to be 41 students for the Informatics Study Program, 24 students for Environmental Engineering, and 16 students for Mechanical Engineering. This research is expected to be a supporting basis for planning new student admissions at ITAF Kupang.
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Copyright (c) 2026 Mohamad Iqbal Ulumando, Orry Adrianus Mokola (Author)
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