Predicting the Eligibility of Scholarship Applicants Using aLogistic Regression Algorithmlgorithm
Authors
-
Eka Fitri Handayani
Universitas Duta Bangsa, Surakarta, Indonesia
-
Nurmalitasari
Universitas Duta Bangsa, Surakarta, Indonesia
-
Sri Sumarlinda
Universitas Duta Bangsa, Surakarta, Indonesia
DOI:
https://doi.org/10.30812/bite.v8i1.6520Keywords:
CRISP-DM, Logistic Regression, Machine Learning, Scholarship Eligibility PredictionAbstract
Background: The scholarship selection process often faces problems because of the large number of applicants that must
be evaluated based on various criteria. This makes the manual selection process take a long time and can lead to subjective
decisions.
Objective: This research aims to build a prediction model for scholarship eligibility using the Logistic Regression algorithm
with the Bagging technique.
Methods: This study uses the CRISP-DM methodology with a dataset of 2,042 student records. The attributes include
GPA, parents’ income, number of dependents, and organizational participation. The data is divided into 80% for training
and 20% for testing to build and evaluate the model.
Result: The research results show that applying the Bagging technique increases accuracy from 82.89% to 83.37%, with a
ROC AUC of 0.9223. The model was then implemented into a web-based prediction system using Flask.
Conclusion: These results show that Logistic Regression with the Bagging technique can be used to support a scholarship
selection process that is faster, more objective, and more consistent.
Downloads
References
[1] M. F. Al-akbari, M. A. Munandar, dan G. Triyono, “Model Optimalisasi Seleksi Penerimaan Beasiswa
Perguruan Tinggi Swasta Menggunakan K-Means dan TOPSIS,” Jurnal Algoritma, vol. 22, no. 2, pp. 2193–
2203, Dec. 9, 2025. doi: 10.33364/algoritma/v.22-2.2531
[2] D. -, “Implementasi sistem pendukung keputusan dalam penentuan penerima beasiswa menggunakan
metode topsis,” Jurnal Teknik, vol. 15, no. 1, Jan. 15, 2026. doi: 10.31000/jt.v15i1.15234
[3] Q. Zirby, N. Nafi’iyah, dan A. S. Budi, “Penerapan Machine Learning untuk Prediksi Penerima Beasiswa
pada Universitas Muhammadiyah Pringsewu,” Jurnal Nasional Komputasi dan Teknologi Informasi
(JNKTI), vol. 8, Oct. 8, 2025.
[4] M. Paramesha et al., “Data-Driven Scholarship Prediction Using Machine Learning for Equitable Education
Allocation,” Frontiers in Collaborative Research, vol. 2, pp. 43–50, 1s Dec. 31, 2024. doi: 10.70162/fcr/
2024/v2/i1/v2i1s06
[5] D. Kurniadi, F. Nuraeni, dan A. F. Hazar, “Aplikasi Sistem Prediksi Mahasiswa Penerima Beasiswa
Berbasis Web dengan Menerapkan Model Klasifikasi K-Nearest Neighbors,” Jurnal Algoritma, vol. 21,
no. 1, pp. 68–79, May 18, 2024. doi: 10.33364/algoritma/v.21-1.1424
[6] A. A. S. Pradhana, K. S. Batubulan, dan I. N. D. Kotama, “Using Random Forest to Classify Financially
Eligible Students for UKT,” Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI), vol. 5,
no. 4, pp. 335–344, 2023. doi: 10.33173/jsikti.250
[7] T. Suprapti et al., “A Decision Tree Model with Grid Search Optimization for Scholarship Recipient
Classification,” Jurnal Teknik Informatika (Jutif ), vol. 6, no. 5, pp. 3800–3813, Oct. 22, 2025. doi:
10.52436/1.jutif.2025.6.5.5235
[8] H. Putra, “Comparative Study of Logistic Regression, Random Forest, and XGBoost for Bank Loan
Approval Classification,” 2025.
[9] U. A. Wizsa dan A. Rahmi, “Classification Of KIP-K Scholarship Using Logistic Regression, Classification
Trees, and Boosting Based On Decision Support System,” Mathline : Jurnal Matematika dan Pendidikan
Matematika, vol. 10, no. 1, pp. 221–235, Feb. 28, 2025. doi: 10.31943/mathline.v10i1.837
[10] J. Jeffry, S. Usman, dan F. Aziz, “Analisis Perilaku Pelanggan menggunakan Metode Ensemble Logistic
Regression,” Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP), vol. 6, no. 2, pp. 90–97, Oct. 16,
2023. doi: 10.34012/jutikomp.v6i2.4258
[11] W. Teguh, “Machine Learning-Based Classification For Selection Of Smart Scholarship Fee Amounts
In The Capital City,” Cerdika: Jurnal Ilmiah Indonesia, vol. 5, no. 2, pp. 515–530, Feb. 19, 2025. doi:
10.59141/cerdika.v5i2.2497
[12] A. Rianti, N. W. A. Majid, dan A. Fauzi, “CRISP-DM: Metodologi Proyek Data Science,” Prosiding
Seminar Nasional Teknologi Informasi dan Bisnis, pp. 106–114, Jul. 25, 2023.
[13] G. Budiyanto dan D. Suryadi, “Model Prediksi dengan Pembelajaran Mesin dalam Pemberian Program
Beasiswa kepada Calon Mahasiswa Baru Program S1 di Perguruan Tinggi Swasta.,” Jurnal Rekayasa
Sistem Industri, vol. 12, no. 2, pp. 187–200, Oct. 25, 2023. doi: 10.26593/jrsi.v12i2.6595.187-200
[14] S. Santiastry, A. Asriyanik, dan W. Apriandari, “Penerapan Algoritma Naive Bayes dan Metode Crisp-DM
dalam Prediksi Hasil Tes Kemampuan Bahasa Inggris Mahasiswa,” JATI (Jurnal Mahasiswa Teknik
Informatika), vol. 8, no. 5, pp. 10 432–10 439, Sep. 23, 2024. doi: 10.36040/jati.v8i5.11069
[15] R. Islam, S. Mazumdar, dan R. Islam, “An Experiment on Feature Selection Using Logistic Regression,”
in 2024 5th Information Communication Technologies Conference (ICTC), Nanjing, China: IEEE, May 10,
2024, pp. 319–324. doi: 10.1109/ICTC61510.2024.10602330
[16] M. Arifin, W. Widowati, dan F. Farikhin, “Optimization of Hyperparameters in Machine Learning for
Enhancing Predictions of Student Academic Performance,” Ing´enierie des syst`emes d information, vol. 28,
no. 3, pp. 575–582, Jun. 30, 2023. doi: 10.18280/isi.280305
[17] T. Nabarian, I. Wahyuni, dan S. E. Farisi, “Penerapan Kerangka CRISP-DM dalam Evaluasi Performa
Logistic Regression dan Decision Tree untuk Prediksi Kelulusan Mahasiswa,” Indonesian Journal Computer
Science, vol. 5, no. 1, pp. 46–55, Apr. 29, 2026. doi: 10.31294/ijcs.v5i1.12502
[18] D. Septasari dan U. I. Kurnia, “Pemodelan Artificial Neural Network Dalam Prediksi Penentu Penerima
Beasiswa,” Jurnal Rekayasa Perangkat Lunak, vol. 4, no. 1, pp. 26–33, Jun. 12, 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eka Fitri Handayani, Nurmalitasari, Sri Sumarlinda
This work is licensed under a Creative Commons Attribution 4.0 International License.
