Regresi Logistik Multinomial untuk Prediksi Kategori Kelulusan Mahasiswa
DOI:
https://doi.org/10.14421/jiska.2023.8.2.102-111Keywords:
Categories, Graduation, Prediction, Logistic Regression, Machine LearningAbstract
Students must meet certain goals to earn a degree but can extend their time at university or drop out (DO). The problem of dropping out of students has become an important issue for tertiary institutions to ensure the success or graduation of students and reduce dropouts. DO can affect the accreditation of the tertiary institution. The quality of higher education institutions in Indonesia is measured based on accreditation from the National Accreditation Board for Higher Education or BAN-PT. One of the main standards measured is the Quality of Students and Graduates. The quality of educational accreditation is measured by the percentage of student graduation and the university's strategy to retain students. To predict student graduation based on graduation time categories, researchers collected academic data from students in 2012-2018 at the Informatics Engineering Study Program, State Islamic University of Maulana Malik Ibrahim Malang. The variables used as predictors are gender, type of entry pathway, and grade point average from semesters one to six. The resulting model was evaluated to obtain an accuracy value of 85.5%, a precision of 78.5%, a recall of 93.9%, and a micro f1-score of 89.8%. An accuracy value of 85.5% indicates that the system can classify properly using the logistic regression model.
References
Agarwal, S. (2013). Data Mining: Data Mining Concepts and Techniques. 2013 International Conference on Machine Intelligence and Research Advancement, 203–207. https://doi.org/10.1109/ICMIRA.2013.45
Agwil, W., Fransiska, H., & Hidayati, N. (2020). Analisis Ketepatan Waktu Lulus Mahasiswa dengan Menggunakan Bagging CART. FIBONACCI: Jurnal Pendidikan Matematika Dan Matematika, 6(2), 155. https://doi.org/10.24853/fbc.6.2.155-166
Al-Balushi, M. S., & Islam, M. M. (2020). Predicting Academic Performance of Students of Sultan Qaboos University, Oman, Using Multilevel Modeling Approach. Far East Journal of Theoretical Statistics, 58(1), 59–76. https://doi.org/10.17654/TS058010059
Alturki, S., Hulpuș, I., & Stuckenschmidt, H. (2022). Predicting Academic Outcomes: A Survey from 2007 Till 2018. Technology, Knowledge and Learning, 27(1), 275–307. https://doi.org/10.1007/s10758-020-09476-0
Asha, P., Vandana, E., Bhavana, E., & Shankar, K. R. (2020). Predicting University Dropout through Data Analysis. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 852–856. https://doi.org/10.1109/ICOEI48184.2020.9142882
Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques. American Scientific Research Journal for Engineering, Technology, and Sciences, 44(1), 122–136. https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/4170
Dalipi, F., Imran, A. S., & Kastrati, Z. (2018). MOOC dropout prediction using machine learning techniques: Review and research challenges. 2018 IEEE Global Engineering Education Conference (EDUCON), 1007–1014. https://doi.org/10.1109/EDUCON.2018.8363340
El-Habil, A. M. (2012). An Application on Multinomial Logistic Regression Model. Pakistan Journal of Statistics and Operation Research, 8(2), 271. https://doi.org/10.18187/pjsor.v8i2.234
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for Multi-Class Classification: an Overview. http://arxiv.org/abs/2008.05756
Handini, D., Hidayat, F., Putri, D. A. V., Rouf, M. R., Anjani, N. R., & Attamimi, A. N. R. (2020). Statistik pendidikan tinggi tahun 2020 (higher education statistics 2020). Sekretariat Direktorat Jenderal Pendidikan Tinggi. https://repositori.kemdikbud.go.id/22653/
Hashim, A. S., Awadh, W. A., & Hamoud, A. K. (2020). Student Performance Prediction Model based on Supervised Machine Learning Algorithms. IOP Conference Series: Materials Science and Engineering, 928(3), 032019. https://doi.org/10.1088/1757-899X/928/3/032019
Hoffait, A.-S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11. https://doi.org/10.1016/j.dss.2017.05.003
Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2018). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. In O. H. T. Lu, J. C. H. Huang, A. Y. Q. Huang, & S. J. H. Yang (Eds.), Learning Analytics (pp. 78–92). Routledge. https://doi.org/10.4324/9780429428500-7
Mayadewi, P., & Rosely, E. (2015). Prediksi Nilai Proyek Akhir Mahasiswa Menggunakan Algoritma Klasifikasi Data Mining. Seminar Nasional Sistem Informasi Indonesia (SESINDO) 2015, 2015. https://is.its.ac.id/pubs/oajis/index.php/home/detail/1582/PREDIKSI-NILAI-PROYEK-AKHIR-MAHASISWA-MENGGUNAKAN-ALGORITMA-KLASIFIKASI-DATA-MINING
Perez, B., Castellanos, C., & Correal, D. (2018). Applying Data Mining Techniques to Predict Student Dropout: A Case Study. 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI), 1–6. https://doi.org/10.1109/ColCACI.2018.8484847
Sari, A. Q., Sukestiyarno, Y., & Agoestanto, A. (2017). Batasan Prasyarat Uji Normalitas Dan Uji Homogenitas pada Model Regresi Linear. Unnes Journal of Mathematics, 6(2), 168–177. https://doi.org/10.15294/UJM.V6I2.11887
Satsangkaryon, S. (2018). Analisis Korelasi Pemanfaatan Hasil Perkembangan Teknologi Informasi Terhadap Tingkat Kelulusan Mahasiswa pada Fakultas Ekonomi Universitas Pakuan Bogor. JIMFE (Jurnal Ilmiah Manajemen Fakultas Ekonomi), 2(1), 73–87. https://doi.org/10.34203/jimfe.v2i1.722
Scott, A. J., Hosmer, D. W., & Lemeshow, S. (1991). Applied Logistic Regression. Biometrics, 47(4), 1632. https://doi.org/10.2307/2532419
Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2017). On predicting academic performance with process mining in learning analytics. Journal of Research in Innovative Teaching & Learning, 10(2), 160–176. https://doi.org/10.1108/JRIT-09-2017-0022
Urrutia-Aguilar, M. E., Fuentes-García, R., Martínez, V. D. M., Beck, E., León, S. O., & Guevara-Guzmán, R. (2016). Logistic Regression Model for the Academic Performance of First-Year Medical Students in the Biomedical Area. Creative Education, 07(15), 2202–2211. https://doi.org/10.4236/ce.2016.715217
Yaacob, W. F. W., Sobri, N. M., Nasir, S. A. M., Yaacob, W. F. W., Norshahidi, N. D., & Husin, W. Z. W. (2020). Predicting Student Drop-Out in Higher Institution Using Data Mining Techniques. Journal of Physics: Conference Series, 1496, 012005. https://doi.org/10.1088/1742-6596/1496/1/012005
Zhang, D., Wang, J., & Zhao, X. (2015). Estimating the Uncertainty of Average F1 Scores. Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 317–320. https://doi.org/10.1145/2808194.2809488
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