PENERAPAN MODEL KLASIFIKASI PADA KELULUSAN MAHASISWA DI UNIVERSITAS PERJUANGAN TASIKMALAYA MENGGUNAKAN ALGORITMA C4.5 - Nugraha, Salsabila Dewi (Jurnal Informatika dan Teknik Elektro Terapan, 2024)
This study addresses the challenge of student accumulation and seeks to predict timely graduation at the University of Perjuangan Tasikmalaya. According to UNESCO, enhancing the quality of education is key to improving a nation's overall quality. Timely graduation is a critical factor in assessing higher education accreditation and educational efficiency. However, predicting student graduation can be challenging. To tackle this issue, this study utilizes data mining techniques with the Decision Tree Algorithm to predict student graduation. Previous research has demonstrated the effectiveness of classification methods across various data types. In this study, the Decision Tree Algorithm yielded impressive results, with an accuracy rate of 92.233% when using the entire dataset as the training set, and 90.24% accuracy with 80% cross-validation. These results highlight the high accuracy of the Decision Tree Algorithm in classifying student graduation outcomes. Consequently, this study will employ the Decision Tree Algorithm to analyze the factors influencing graduation at the University of Perjuangan Tasikmalaya, providing insights into improving educational outcomes and supporting timely student graduation.