COMPARISON OF DECISION TREE AND RANDOM FOREST ALGORITHMS IN PREDICTING STUDENT GRADUATION BASED ON ACADEMIC DATA
Keywords:
Decision Tree, Random Forest, Graduation Prediction, Academic Data, GPA, Credit HourAbstract
This research aims to compare the performance of the Decision Tree and Random Forest algorithms in predicting student graduation based on academic data. By utilizing data such as Grade Point Average (GPA), the number of credit hours, and course grades, this study focuses on analyzing the accuracy of both algorithms in predicting students who are at risk of not graduating on time. The results of the study indicate that the Random Forest algorithm achieves higher accuracy compared to the Decision Tree, particularly in terms of recall and precision. While Decision Tree is simpler and easier to interpret, it tends to have overfitting issues that can affect prediction results. In contrast, Random Forest overcomes these issues by producing more stable predictions through an ensemble process. This study is expected to contribute to the development of student graduation prediction systems in educational institutions. As such, institutions can use these findings as a foundation for designing intervention strategies for students at risk of not graduating on time.
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References
Amri, Z., Kusrini, K., & Kusnawi, K. (2023). Prediksi Tingkat Kelulusan Mahasiswa menggunakan Algoritma Naïve Bayes, Decision Tree, ANN, KNN, dan SVM. Edumatic: Jurnal Pendidikan Informatika, 7(2), 187–196. https://doi.org/10.29408/edumatic.v7i2.18620
Budiyantara, A., & A, I. (2018). Prediksi Mahasiswa Lulus Tepat Waktu. Infotech: Journal of Technology Information, 5(2), 7–13. https://doi.org/10.37365/it.v5i2.39
Darmawan, A., Yudhisari, I., Anwari, A., & Makruf, M. (2023). Pola Prediksi Kelulusan Siswa Madrasah Aliyah Swasta dengan Support Vector Machine dan Random Forest. Jurnal Minfo Polgan, 12(1), 387–400. https://doi.org/10.33395/jmp.v12i1.12388
Latifah, S. L. S. N. H. (2020). Prediksi Prestasi Akademik Mahasiswa Menggunakan Random Forest dan C.45. VIII(1), 47–52. www.bsi.ac.id
Linawati, S., Nurdiani, S., Handayani, K., & Latifah, L. (2020). Prediksi Prestasi Akademik Mahasiswa Menggunakan Algoritma Random Forest Dan C4.5. Jurnal Khatulistiwa Informatika, 8(1), 47–52. https://doi.org/10.31294/jki.v8i1.7827
Permatasari, R. P. (2021). Implementasi algoritma decision tree untuk prediksi kelulusan mahasiswa tepat waktu laporan skripsi.
Plaosan, van S. (2019). Algoritma Random Forest. Http://Learningbox.Coffeecup.Com/05_2_Randomforest.Html, 18(1), 10–14.
Prediksi kelulusan pelajar menggunakan decision tree. (2023). 90–94.
Satrio Junaidi, Valicia Anggela, R., & Kariman, D. (2024). Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwork (ANN). Journal of Applied Computer Science and Technology, 5(1), 109–119. https://doi.org/10.52158/jacost.v5i1.489
Wahyudi, A. (2023). Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Decision Tree Dan Naïve Bayes. Jurnal Permata Indonesia, 14(2), 132–138. https://doi.org/10.59737/jpi.v14i2.276
Zeniarja, J., Salam, A., & Ma’ruf, F. A. (2022). Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa. Jurnal Rekayasa Elektrika, 18(2), 102–108. https://doi.org/10.17529/jre.v18i2.24047
