Predicting Student On-Time Graduation Using Particle Swarm Optimization and Random Forest Algorithms

Arif Rahman; Deni Mahdiana; Achmad Fauzi

doi:10.24014/ijaidm.v8i1.33577

Predicting Student On-Time Graduation Using Particle Swarm Optimization and Random Forest Algorithms

Arif Rahman, Deni Mahdiana, Achmad Fauzi

Abstract

Higher education plays a crucial role in human resource development and national progress. A key indicator of educational quality is students' ability to graduate on time. Delays in graduation can lower the quality of higher education. Various academic and non-academic factors influence timely graduation rates. At Universitas Islam Syekh Yusuf, the trend of students graduating beyond the expected timeframe has risen over the past three years. However, the university lacks insight into the factors contributing to these delays. This research aims to identify factors causing delayed graduation using PSO and Random Forest to predict student graduation outcomes. The application of PSO reveals key factors influencing timely graduation, including study program, student active status, student leave of absence status, inactive status for semester 1, GPA1, and credit hours in semesters 1 and 2. Evaluation results show that using PSO and Random Forest to predict timely graduation achieves high accuracy (99.63%), precision (99.77%), recall (99.65%), and F1 score (99.71%).

Keywords

Classification; Feature Selection; Particle Swarm Optimization; Random Forest; Student Graduation

Full Text:

PDF

References

I. Irawan, R. Qisthiano, M. Syahril, and P. M. Jakak, “Optimasi Prediksi Kelulusan Tepat Waktu: Studi Perbandingan Algoritma Random Forest dan Algoritma K-NN Berbasis PSO,” Jurnal Pengembangan Sistem Informasi dan Informatika, vol. 4, no. 4, 2023, doi: 10.47747/jpsii.v4i4.1374.

M. Ridwan, “Sistem Rekomendasi Proses Kelulusan Mahasiswa Berbasis Algoritma Klasifikasi C4.5,” Jurnal Ilmiah Informatika, vol. Volume 2, 2017, doi: 10.35316/jimi.v2i1.460.

E. Haryatmi and S. P. Hervianti, “Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, 2021, doi: 10.29207/resti.v5i2.3007.

Suhardjono, G. Wijaya, and A. Hamid, “Prediksi Waktu Kelulusan Mahasiswa Menggunakan SVM Berbasis PSO,” Bianglala Informatika, vol. 7, no. 2, 2019, doi: 10.31294/bi.v7i2.6654.

C. N. Dengen, K. Kusrini, and E. T. Luthfi, “Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” SISFOTENIKA, vol. 10, no. 1, 2020, doi: 10.30700/jst.v10i1.484.

D. A. Putra and M. Kamayani, “Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Naive Bayes di Program Studi Teknik Informatika UHAMKA,” Prosiding Seminar Nasional Teknoka, vol. 5, 2020, doi: 10.22236/teknoka.v5i.331.

S. Devella, Y. Yohannes, and F. N. Rahmawati, “Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 7, no. 2, 2020, doi: 10.35957/jatisi.v7i2.289.

B. A. Arifiyani and R. S. Samosir, “Sistem Simulasi Prediksi Profil Kelulusan Mahasiswa Dengan Decison Tree,” Kalbiscentia, vol. 5, no. 2, 2018.

L. Breiman, “Random Forests,” Mach Learn, vol. 45, pp. 5–32, 2001, Accessed: Jan. 30, 2024. [Online]. Available: https://doi.org/10.1023/A:1010933404324

I. D. Mienye, Y. Sun, and Z. Wang, “An improved ensemble learning approach for the prediction of heart disease risk,” Inform Med Unlocked, vol. 20, 2020, doi: 10.1016/j.imu.2020.100402.

B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms,” Applied Soft Computing Journal, vol. 18, 2014, doi: 10.1016/j.asoc.2013.09.018.

T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, “Particle Swarm Optimization: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 10031–10061, 2022, doi: 10.1109/ACCESS.2022.3142859.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques, Fourth Edition., vol. 4. Todd Green, 2016. doi: 10.1016/C2015-0-02071-8.

Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Informatika, vol. 2, no. 2, 2017, doi: 10.22202/ei.2016.v2i2.1465.

A. Ishaq et al., “Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3064084.

M. Schonlau and R. Y. Zou, “The random forest algorithm for statistical learning,” Stata Journal, vol. 20, no. 1, 2020, doi: 10.1177/1536867X20909688.

D. A. Rachmawati, N. A. Ibadurrahman, J. Zeniarja, and N. Hendriyanto, “Implementation of The Random Forest Algorithm in Classifying The Accuracy of Graduation Time For Computer Engineering Students at Dian Nuswantoro University,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 3, pp. 565–572, Jun. 2023, doi: 10.52436/1.jutif.2023.4.3.920.

M. Y. Khan, A. Qayoom, M. S. Nizami, M. S. Siddiqui, S. Wasi, and S. M. K. U. R. Raazi, “Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding-Based Deep Learning Techniques,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/2553199.

E. Ismanto and M. Novalia, “Komparasi Kinerja Algoritma C4.5, Random Forest, dan Gradient Boosting untuk Klasifikasi Komoditas,” Techno.Com, vol. 20, no. 3, pp. 400–410, Aug. 2021, doi: 10.33633/tc.v20i3.4576.

U. M. Khaire and R. Dhanalakshmi, “Stability of feature selection algorithm: A review,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, 2022, doi: 10.1016/j.jksuci.2019.06.012.

M. H. Aghdam and S. Heidari, “Feature selection using particle swarm optimization in text categorization,” Journal of Artificial Intelligence and Soft Computing Research, vol. 5, no. 4, 2015, doi: 10.1515/jaiscr-2015-0031.

H. S. Baruah, J. Thakur, S. Sarmah, and N. Hoque, “A Feature Selection Method using PSO-MI,” in 2020 International Conference on Computational Performance Evaluation, ComPE 2020, IEEE, Jul. 2020. doi: 10.1109/ComPE49325.2020.9200034.

M. Y. Kurniawan and M. E. Rosadi, “Optimasi Decision Tree Menggunakan Particle Swarm Optimization pada Data Siswa Putus Sekolah,” Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM), vol. 2, no. 1, 2017, doi: 10.20527/jtiulm.v2i1.13.

H. Herlinda, M. Itqan Mazdadi, D. Kartini, and I. Budiman, “Implementation of Particle Swarm Optimization on Sentiment Analysis of Cyberbullying using Random Forest,” JUITA: Jurnal Informatika, vol. 11, no. 2, pp. 301–309, 2023, doi: 10.30595/juita.v11i2.17920.

E. Purnamasari, D. Palupi Rini, and Sukemi, “Seleksi Fitur menggunakan Algoritma Particle Swarm Optimization pada Klasifikasi Kelulusan Mahasiswa dengan Metode Naive Bayes,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 3, pp. 469–475, 2017, doi: 10.29207/resti.v4i3.1833.

I. Ahmad, “Feature selection using particle swarm optimization in intrusion detection,” Int J Distrib Sens Netw, vol. 2015, 2015, doi: 10.1155/2015/806954.

G. Zeng, “On the confusion matrix in credit scoring and its analytical properties,” Commun Stat Theory Methods, vol. 49, no. 9, 2020, doi: 10.1080/03610926.2019.1568485.

P. Lalwani, M. K. Mishra, J. S. Chadha, and P. Sethi, “Customer churn prediction system: a machine learning approach,” Computing, vol. 104, no. 2, 2022, doi: 10.1007/s00607-021-00908-y.

DOI: http://dx.doi.org/10.24014/ijaidm.v8i1.33577

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats