Detecting Late Payments by Students with Random Forest and Particle Swarm Optimisation

Abdul Hamid; Sugiono Sugiono; Titin Prihatin; Henny Destiana; Astrilyana Astrilyana

doi:10.24036/voteteknika.v13i4.135375

Detecting Late Payments by Students with Random Forest and Particle Swarm Optimisation

Abdul Hamid⁽¹⁾, Sugiono Sugiono⁽²⁾, Titin Prihatin⁽³⁾, Henny Destiana⁽⁴⁾, Astrilyana Astrilyana⁽⁵⁾,
(1) Universitas Bina Sarana Informatika

Indonesia
(2) Universitas Bina Sarana Informatika

Indonesia
(3) Universitas Bina Sarana Informatika

Indonesia
(4) Universitas Bina Sarana Informatika

Indonesia
(5) Universitas Bina Sarana Informatika

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/voteteknika.v13i4.135375

Full Text:

Language : en

Abstract

Delays in tuition fee payments are a crucial problem commonly faced by private universities in Indonesia. This problem not only complicates campus financial management, but also has the potential to hinder the smooth running of student studies, such as leave of absence or discontinuation of studies. To date, there are not many predictive systems used to detect potential late payments early, especially the Random Forest (RF) model with Particle Swarm Optimisation (PSO). Therefore, this study aims to develop a predictive model for student payment delays by utilising the RF algorithm optimised using the PSO feature selection method. The Dataset used consists of 15,697 student data covering academic and administrative attributes. Pre-processing was carried out to convert categorical data into numerical form so that it could be processed by the classification algorithm. The evaluation results show that the RF model without optimisation produces an accuracy of 97.37%, precision of 100%, recall of 18.68%, and AUC of 0.825 ± 0.020. After feature selection with PSO, the model performance improved, with an accuracy of 98.83%, precision of 98.20%, recall of 25.40%, and AUC remaining stable at 0.825 ± 0.035. The most influential attributes in the classification were semester, leave status, studying while working, and father's occupation. The results of this study indicate that the combination of RF and PSO can produce an efficient and accurate prediction model, which can be used as a decision-making tool in higher education administration management.

Keywords— Late Payments, Classification, PSO, Random Forest, Feature Selection.

References

Ridwansyah, M. Iqbal, H. Destiana, Sugiono, and A. Hamid, “Data Mining Berbasis Machine Learning Untuk Analitik Prediktif Dalam Kelulusan,” semanTIK, vol. 10, no. 2, pp. 1–10, 2024, doi: https://doi.org/10.55679/semantik.v10i2.67.

S. Anam, M. R. A. Putra, Z. Fitriah, I. Yanti, N. Hidayat, and D. M. Mahanani, “Health Claim Insurance Prediction Using Support Vector Machine With Particle Swarm Optimization,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 0797–0806, 2023, doi: 10.30598/barekengvol17iss2pp0797-0806.

N. W. D. Ayuni, N. N. Lasmini, and K. C. Dewi, “Predicting financial distress of property and real estate companies using optimized support vector machine-particle swarm optimization (SVM-PSO),” Bull. Soc. Informatics Theory Appl., vol. 8, no. 1, pp. 97–106, 2024.

W. Li, “Design of Financial Crisis Early Warning Model Based on PSO-SVM Algorithm,” Math. Probl. Eng., pp. 1–8, 2022, doi: https://doi.org/10.1155/2022/3241802.

A. H. Kahfi, T. Prihatin, Yudhistira, A. Sudradjat, and G. Wijaya, “THE RIGHT STEPS TOWARDS GRADUATION: NB-PSO SMART COMBINATION FOR STUDENT GRADUATION PREDICTION,” J. Tek. Inform., vol. 5, no. 2, pp. 607–614, 2024, doi: https://doi.org/10.52436/1.jutif.2024.5.2.1889.

T. Azhima, Y. Siswa, and W. J. Pranoto, “Implementasi Seleksi Fitur Information Gain Ratio Pada Algoritma Random Forest Untuk Model Data Klasifikasi Pembayaran Kuliah,” Din. Inform., vol. 15, no. 1, pp. 41–49, 2023.

M. Norhalimi and T. A. Y. Siswa, “Optimasi Seleksi Fitur Information Gain pada Algoritma Naïve Bayes dan K-Nearest Neighbor,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 7, no. 3, pp. 237–255, 2022, doi: 10.14421/jiska.2022.7.3.237-255.

H. Umar, R. Kusumawati, M. Imamudin, and M. A. Rohman, “Klasifikasi Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan Menggunakan Algoritma Naïve Bayes dan Support Vector Machine,” TIN Terap. Inform. Nusant., vol. 4, no. 11, pp. 709–718, 2024, doi: 10.47065/tin.v4i11.4969.

M. R. Akhmad and T. A. Y. Siswa, “Implementasi K-Nearest Neighbor Dalam Memprediksi Keterlambatan Pembayaran Biaya Kuliah Di Perguruan Tinggi,” Progresif J. Ilm. Komput., vol. 18, no. 2, p. 185, 2022, doi: 10.35889/progresif.v18i2.921.

M. Mubarokah, D. Aditya Nugraha, and A. Yunus, “Penerapan Algoritma C4.5 dalam Memprediksi Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan,” J. Ris. Mhs. Bid. Teknol. Inf., vol. 7, no. 1, pp. 1–5, 2024, [Online]. Available: https://ejournal.unikama.ac.id/index.php/JFTI.

T. A. Y. Siswa and R. P. Wibowo, “Komparasi Metode Seleksi Fitur Dalam Prediksi Keterlambatan Pembayaran Biaya Kuliah,” Teknika, vol. 12, no. 1, pp. 73–82, 2023, doi: 10.34148/teknika.v12i1.601.

M. J. Budiman and Fanny Jouke Doringin, “PENERAPAN ALGORITMA C5.0 DALAM MEMPREDIKSI KETERLAMBATAN PEMBAYARAN BIAYA KULIAH DI UNKRISWINA SUMBA,” J. Ilmu Komput. Revolusioner, vol. 8, no. 6, 2024.

H. Nurdin, I. Nawawi, A. Wuryanto, D. Yuliandari, and H. Sugiarto, “Prediksi Keterlambatan Pembayaran Mahasiswa untuk Mitigasi Risiko Cuti Menggunakan SVM Optimasi PSO,” J. Apl. Sains, Inf. , Elektron. dan Komput., vol. 7, no. 1, pp. 1–11, 2025, doi: https://doi.org/10.26905/jasiek.v7i1.15483.

W. Widiarina, K. Mariskhana, I. D. Sintawati, R. Ningsih, and A. Natong, “Integrating Random Forest and Particle Swarm Optimization for Chronic Kidney Disease Diagnosis,” J. Vocat. Tek. Elektron. dan Inform., vol. 13, no. 2, pp. 110–117, 2025, doi: 10.24036/voteteknika.v13i2.133634.

A. Hamid and Ridwansyah, “Optimizing Heart Failure Detection : A Comparison between Naive Bayes and Particle Swarm Optimization,” Paradigma, vol. 26, no. 1, pp. 30–36, 2024, doi: https://doi.org/10.31294/p.v26i1.3284.

Sumarna, I. Nawawi, Suhardjono, Hari Sugiarto, and D. Yuliandari, “MENINGKATKAN AKURASI PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN METODE ALGORITMA GENETIKA,” J. Inform. Manaj. dan Komput., vol. 16, no. 2, 2024, doi: http://dx.doi.org/10.36723/juri.v16i2.706.

Sumarna, Sartini, W. E. Pangesti, R. Suryadithia, and V. Riyanto, “Decision Tree Optimization in Heart Failure Diagnostics: a Particle Swarm Optimization Approach,” J. Tek. Inform., vol. 5, no. 3, pp. 739–746, 2024, doi: https://doi.org/10.52436/1.jutif.2024.5.3.1815.