Model Klasifikasi Untuk Menentukan Kesiapan Kerja Mahasiswa Dan Kelulusan Tepat Waktu Dengan Metode Machine Learning

Abstract

Graduate on time is one of the indicators of a student’s success in obtaining their degrees. However, graduating on time also raises several issues, one of which is related to the employability of students after they graduate. Previous study using Three Different machine learning techniques. found that the accuracy level using these algorithms yielded satisfactory results. However, these research results were obtained using only one type of algorithm, so there is no comparison to determine whether the algorithm used is the most optimal. After conducting a classification model with KNIME using three machine learning algorithms, namely decision tree, naïve Bayes, and KNN, it was found that the Naive Bayes algorithm has the highest accuracy (0.537) compared to Decision Tree (0.509) and KNN (0.511).