Klasifikasi Status Akademik Mahasiswa Menggunakan Decision Tree
DOI:
https://doi.org/10.35473/jamastika.v4i2.4523Abstract
Penelitian ini membahas pemodelan klasifikasi status mahasiswa berdasarkan data akademik dengan menerapkan algoritma Decision Tree menggunakan RapidMiner. Data yang dianalisis meliputi variabel seperti indeks prestasi kumulatif (IPK), jumlah SKS, lama studi, dan faktor usia. Proses klasifikasi dilakukan melalui tahapan pra-pemrosesan data, pembangunan model, serta evaluasi menggunakan confusion matrix dan metrik akurasi. Hasil analisis menunjukkan bahwa model yang dibentuk mampu mengidentifikasi pola-pola yang memengaruhi kelulusan mahasiswa, dengan tingkat akurasi mencapai 66,67%. Pendekatan ini diharapkan dapat menjadi alat bantu strategis dalam mendukung pengambilan keputusan akademik berbasis data dan memperbaiki sistem pemantauan kelulusan di lingkungan perguruan tinggi.
Kata Kunci: Klasifikasi, Decision tree, RapidMinner, Status Mahasiswa, Data Akademik
This study focuses on modeling the classification of student graduation status using the Decision Tree algorithm implemented through RapidMiner. The academic data includes variables such as Grade Point Average (GPA), total credit hours, study duration, and age. The classification process involves data preprocessing, model building, and evaluation using a confusion matrix and accuracy metrics. The results indicate that the model effectively identifies patterns influencing student graduation outcomes, achieving an accuracy rate of 66.67%. This approach is expected to serve as a strategic tool to support data-driven academic decision-making and to improve student progress monitoring systems in higher education institutions.
Keywords: classification, decision tree, RapidMiner, student status, academic data
References
Y. E. Yuspita, R. Okra, and M. Rezeki, “Penerapan Algoritma Klasifikasi Untuk Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Rappidminer,” Djtechno J. Teknol. Inf., vol. 6, no. 1, pp. 376– 388, 2025, doi: 10.46576/djtechno.v6i1.6169.
R. V. A. Suyanto, Eduard Rusdianto, and Ernawati, “Penerapan Algoritma Decision Tree C4.5 dan Metode AdaBoost Untuk Prediksi Kelulusan Mahasiswa,” J. Inform. Atma Jogja, vol. 5, no. 1, pp. 75–86, 2024, doi: 10.24002/jiaj.v5i1.8646.
H. Situmorang, “Sistem Informasi Pengelolahan Data Alumni Berbasis Web (Studi Pada Fakultas Sain, Teknologi Dan Informasi) Universitas Sari Mutiara Indonesia,” J. Mahajana Inf., vol. 4, no. 1, pp. 28–41, 2019, doi: 10.51544/jurnalmi.v4i1.724.
U. Al Faruq, M. Ainun Naja Fauzi, I. Fatayasya, E. Daniati, A. Ristyawan, and N. PGRI Kediri, “Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) 131 Prediksi Data Kelulusan Mahasiswa Dengan Metode Decision Tree menggunakan Rapidminer,” Agustus, vol. 7, pp. 2549– 7952, 2023.
M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,” Eeccis, vol. 7, no. 1, pp. 59–64, 2013, doi: 10.1038/hdy.2009.180.
A. Asro, J. Chaidir, C. Cahairuddin, and J. Friadi, “Evaluasi Kinerja Algoritma Klasifikasi dalam Studi Kasus Prediksi Kelulusan di Universitas XYZ,” Zo. Tek. J. Ilm., vol. 19, no. 1, pp. 15–22, 2025, doi: 10.37776/zt.v19i1.1674.
G. A. Mundok, Amiruddin, and Zulfrianto, “Klasifikasi Waktu Kelulusan Mahasiswa Menggunakan Metode Decision Tree,” J. Ilm. Ilmu Komput., vol. 3, no. 1, pp. 31–36, 2024.
Al-Tameemi, G., Xue, J., Hadi Ali, I., & Ajit, S. (2024). A Hybrid Machine Learning Approach for Predicting Student Performance Using Multi-class Educational Datasets. Procedia Computer Science, 238, 888–895.
Matzavela, V., & Alepis, E. (2021). Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments. Computers and Education: Artificial Intelligence, 2, 100035.
Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61–75.
Ahmed, M. (2024). Student Performance Prediction Using Machine Learning Algorithms. Applied Computational Intelligence and Soft Computing.
Injadat, M. N., Moubayed, A., Bou Nassif, A., & Shami, A. (2020). Systematic Ensemble Model Selection Approach for Educational Data Mining. arXiv.
Yekun, E. A., & Teklay, A. (2019). Student Performance Prediction with Optimum Multilabel Ensemble Model. arXiv.
Wang, Y., Ding, A., Guan, K., Wu, S., & Du, Y. (2021). Graph-based Ensemble Machine Learning for Student Performance Prediction. arXiv.
