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All Journal Jurnal Sistem Informasi dan Sistem Komputer Jurnal Agroteknologi Pertanian dan Publikasi Riset Ilmiah (JAPPRI) Jurnal Ilmu Komputer dan Informatika PERFECT: Journal of Smart Algorithms SULIWA: Jurnal Multidisiplin Teknik, Sains, Pendidikan dan Teknologi
Mohamad Iqbal Ulumando
Institut Teknologi Alberth Foenay Kupang, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Other

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Journal : perfect journal of smart algorithms

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ITAF Kupang New Student Admission Prediction Using The Random Forest Method Mohamad Iqbal Ulumando; Orry Adrianus Mokola
PERFECT: Journal of Smart Algorithms Vol. 3 No. 2 (2026): PERFECT: Journal of Smart Algorithms, Article Research July 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i2.284

Abstract

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.
Prediction Of Repeating Object-Oriented Programming Course for Informatics Students at ITAF Kupang Using Extreme Gradient Boosting (XGBoost) Mohamad Iqbal Ulumando
PERFECT: Journal of Smart Algorithms Vol. 3 No. 2 (2026): PERFECT: Journal of Smart Algorithms, Article Research July 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i2.285

Abstract

The Object-Oriented Programming course is one of the core courses in the Informatics Study Program which has a fairly high level of difficulty so that some students have the potential to fail and have to repeat the course. This study aims to build a prediction model for students of the Informatics Study Program at ITAF Kupang who have the potential to repeat the Object-Oriented Programming course using the Extreme Gradient Boosting (XGBoost) algorithm based on student learning behavior data. The data used amounted to 60 students with variables including attendance, assignment grades, accuracy of assignment submission, discussion participation, quiz scores, practicum activities, and mid-term/final exam scores. The research stages include data collection, data preprocessing, training and testing data distribution, XGBoost model training, and model evaluation using Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results of the study showed that the XGBoost model was able to perform good classification with an Accuracy value of 83.33%, Precision of 80.00%, Recall of 80.00%, and F1-Score of 80.00%. Feature importance analysis showed that quiz scores were the most influential factor in students' potential to repeat courses, followed by mid-term/final exam scores and assignment scores. The results of the study proved that student learning behavior data can be used to build an early warning system that helps lecturers and study programs identify at-risk students early on so that more effective academic mentoring can be provided.
Co-Authors Cyrillus Yuda Mardiyanto Pike Merlin K. Talnoni Orry Adrianus Mokola Risni Stefani