Penerapan Algoritma Decision Tree untuk Prediksi Kelulusan Mahasiswa Berdasarkan Data Akademik Menggunakan RapidMiner - Laela Nur Rohmah; Sara Khusnul Mumtazah; Alvina Damayanti; Amali (Jurnal SIGMA, 2024)
Higher education has an important role in the long-term development of each individual. One of the most important indicators of success for a high-performing educational institution is student achievement. There are several factors that might influence student achievement, including academic, demographic, and socioeconomic factors. This study employs the Decision Tree algorithm, which is one of several effective algorithms for making predictions or analyzing large amounts of data. This study aims to determine whether the Decision Tree algorithm can be used to predict student achievement by gathering information on accuracy, precision, and recall obtained during data collection. This study used RapidMiner tools to create a Decision Tree model and was carried out with the following steps: data collection, data analysis, Decision Tree modeling, method development, and results evaluation. Data collection on the dataset will be divided into two parts: 70% for training and 30% testing. The results of the study on the decision tree algorithm show that it has a good performance with a high accuracy of 73.17%. It also performs well in predicting graduate students with a precision of 74.05% and a recall of 93.82%, as well as dropout students with a precision of 73.02% and a recall of 80.05%.