Bilah Samping Artikel

Telah diserahkan
September 25, 2025
Diterbitkan
Maret 30, 2026
Unduh
pdf (English)
Statistik
Baca Penghitung : 0 Unduh : 0

Isi Artikel Utama

Abstrak

Education plays an important role in improving the quality of human resources and supporting a country’s progress toward becoming a developed nation. Higher education institutions serve as one of the providers of formal education, where the quality of these institutions is measured through accreditation. One of the key indicators influencing accreditation is the outcomes and achievements of the Tri Dharma of higher education, which include the timeliness of student graduation. This study aims to compare models for predicting on-time student graduation using three machine learning algorithms, namely Decision Tree, Naïve Bayes, and Support Vector Machine (SVM), as well as their combination through the Ensemble Voting method. The prediction is based on historical grade data from courses taken during semesters one to four. The research methodology adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used in this study consists of 2,471 records with 11 attributes. Data preprocessing was conducted through data cleaning and class balancing using under sampling techniques. The results indicate that the Ensemble Voting model using the Soft Voting method achieves the best performance, with an accuracy of 91.80%, precision of 91.87%, and recall of 91.80%, outperforming the individual models of Decision Tree, Naïve Bayes, and SVM. The implementation of this model can be utilized to predict students’ on-time graduation based on course grade inputs. Therefore, this research can serve as a supporting tool for early detection of potential delays in student graduation.

Rincian Artikel

Referensi

  1. Amri, Z., Kusrini, K. dan Kusnawi, K. (2023) ‘Prediksi Tingkat Kelulusan Mahasiswa menggunakan Algoritma Naïve Bayes, Decision Tree, ANN, KNN, dan SVM’, Edumatic: Jurnal Pendidikan Informatika, 7(2), pp. 187–196. Available at: https://doi.org/10.29408/edumatic.v7i2.18620.
  2. Arifin, Z., Rahman, D.F., Rintyarna, B.S. dan Daryanto, D. (2023) ‘Penerapan Algoritma Support Vector Machine Berbasis Kernel Radial Basis Function dalam Klasifikasi Sel Kanker’, BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer, 4(2), pp. 100–106. Available at: https://doi.org/10.37148/bios.v4i2.165.
  3. BPS 2022 (2023) ‘Statistik Pendidikan 2023’, Badan Pusat Statistik, 1101001, p. 790.
  4. Budiyantara (2019) ‘Prediksi Mahasiswa Lulus Tepat Waktu Menggunakan Algoritma Decision Tree (C4.5) Pada STMIK Widuri Jakarta’, Infotech, Journal of Technology Information, 5(2), pp. 1–7.
  5. Indahyanti, U., Azizah, N.L. dan Setiawan, H. (2022) ‘Pendekatan Ensemble Learning Untuk Meningkatkan Akurasi Prediksi Kinerja Akademik Mahasiswa’, Jurnal Sains dan Informatika, 8(2). Available at: https://doi.org/10.34128/jsi.v8i2.459.
  6. Mukti, M.W. (2022) ‘Prediksi Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma C4.5 Pada STMIK Dharma Wacana’, International Research on Big-Data and Computer Technology: I-Robot, 6(2), pp. 25–29. Available at: https://doi.org/10.53514/ir.v6i2.326.
  7. Munandar, A., Baihaqi, W.M. dan Nurhopipah, A. (2023) ‘A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients’, ILKOM Jurnal Ilmiah, 15(2), pp. 344–352.
  8. Nazifah, N., Prianto, C. dan Id, C.A. (2023) ‘Analisis Perbandingan Decision Tree Algoritma C4.5 dengan algoritma lainnya: Sistematic Literature Review’. Available at: https://ejurnalunsam.id/index.php/jicom/.
  9. Nurislamiaty, V.A. dan Rozi, A.F. (2021) ‘Prediksi Kelulusan Mahasiswa Fakultas Teknologi Informasi Umby Menggunakan Metode Decision Tree Penerapan Algoritma C4. 5’, Journal Of …, 5, pp. 1–8.
  10. Parteek Bhatia (2019) Data Mining Data Warehouseing Principales and Practical Techniques.
  11. Rahman, A., Zaman, S., Parvej, S., Shill, P.C., Salim, M.S. dan Das, D. (2025) ‘Fake News Detection: Exploring the Efficiency of Soft and Hard Voting Ensemble’, in Procedia Computer Science. Elsevier B.V., pp. 748–757. Available at: https://doi.org/10.1016/j.procs.2025.01.035.
  12. Rohmawan, E.P. (2018) ‘Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Decision Tree dan Artificial Neural Network’, jurnal ilmiah MATRIK, p. 3.
  13. Sasongko, D. (2019) ‘Akreditasi Perguruan Tinggi Kriteria dan Prosedur 3.0’, Badan Akreditasi Nasional Perguruan Tinggi, pp. 1–21.
  14. Sulianta, F. (2024) ‘Buku Dasar Data Mining from A to Z’, (January).
  15. Suratini (2017) ‘Pengaruh Pendidikan Dalam Meningkatkan Kualitas Sumber Daya Manusia di Indonesia’, Future: Jurnal Manajemen dan Akuntansi, 5(1), pp. 68–84.
  16. Tan, P.-N., Steinback, M. dan Kumar, V. (2014) Introduction to Data Mining, Pearson Education Limited. Available at: https://doi.org/10.1016/b978-155558242-5/50003-6.
  17. Tholib, A., Fadli Hidayat, M.N., Yono, S., Wulanningrum, R. dan Daniati, E. (2023) ‘Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms’, International Journal of Engineering and Computer Science Applications (IJECSA), 2(2), pp. 65–72. Available at: https://doi.org/10.30812/ijecsa.v2i2.3364.
  18. Yuni, R., Putra, P.D. dan Hutabarat, D.L. (2020) ‘Sinergi Indonesia Menuju Negara Maju’, Prosiding WEBINAR Fakultas Ekonomi Universitas Negeri Medan, pp. 35–42.