Logistic Regression Model for Predicting SNBP Admission Based on Academic Data
Abstract
The National Selection Based on Achievement (SNBP) is a crucial pathway for prospective students to access higher education; however, the uncertainty surrounding admission outcomes often causes anxiety among prospective students. This study aims to develop an SNBP admission prediction model based on logistic regression using academic data from students at the Darul Arafah Raya Islamic Boarding School. The method used is binary logistic regression with parameter estimation via the Newton-Raphson method. The research data consists of 261 academic records of students from 2022 to 2025, divided into training and testing datasets. Model evaluation was conducted using accuracy, precision, recall, F1 score, and AUC-ROC metrics. The results show that the model achieved convergence at the seventh iteration with an accuracy rate of 81.25 percent. The precision and recall values were 82.35 percent, respectively, while the AUC-ROC value was 0.9049, which falls into the “good classification” category. It can be concluded that the logistic regression model is effective for predicting SNBP graduation based on average report card scores and is suitable for implementation as a decision support system for students in estimating their admission chances.
Downloads
References
S. A. A. Kharis and A. H. A. Zili, “Learning Analytics dan Educational Data Mining pada Data Pendidikan,” Jurnal Riset Pembelajaran Matematika Sekolah, vol. 6, no. 1, pp. 12–20, Mar. 2022, doi: 10.21009/jrpms.061.02.
M. Yağcı, “Educational data mining: prediction of students’ academic performance using machine learning algorithms,” Smart Learning Environments, vol. 9, no. 1, p. 11, Dec. 2022, doi: 10.1186/s40561-022-00192-z.
C. Chandra, I. Fenriana, and R. Rimbawan, “Implementasi Data Mining Untuk Mengetahui Pola Pembelian Pelanggan Pada Produk Vin’s Cafe Dengan Algoritma Apriori Dan Pengujian Kualitas Melalui Metode ISO 9126,” ALGOR, vol. 4, no. 1, pp. 11–20, Sep. 2022, doi: 10.31253/algor.v4i1.1538.
Supangat and Rafif Giovanni, “Evaluasi Tingkat Persaingan Siswa Dalam Seleksi Nasional Masuk Perguruan Tinggi Negeri Menggunakan Algoritma Naive Bayes,” Journal of Scientech Research and Development, vol. 6, no. 1, pp. 1055–1068, Jul. 2024, doi: 10.56670/jsrd.v6i1.337.
I. Hanum, Y. Sholva, H. Sastypratiwi, and F. Asrin, “Model Prediksi Keketatan Lolos SNMPTN Menggunakan Algoritma K-Nearest Neighbor,” Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika, vol. 6, no. 2, pp. 179–190, Jul. 2023, doi: 10.47324/ilkominfo.v6i2.205.
A. Tripena, R. Maharsi, Y. Lianawati, and A. A. Setyawan, “Analisis Faktor-Faktor Yang Mempengaruhi Kemiskinan Rumah Tangga Di Desa Kotayasa Melalui Pendekatan Regresi Logistik Biner,” Jurnal Elektro Luceat, vol. 9, no. 2, 2023.
N. K. Hasibuan, S. Dur, and I. Husein, “Faktor Penyebab Penyakit Diabetes Melitus dengan Metode Regresi Logistik,” G-Tech: Jurnal Teknologi Terapan, vol. 6, no. 2, pp. 257–264, Sep. 2022, doi: 10.33379/gtech.v6i2.1696.
R. Prabowo, H. Sujaini, and T. Rismawan, “Analisis Sentimen Pengguna Twitter Terhadap Kasus COVID-19 di Indonesia Menggunakan Metode Regresi Logistik Multinomial,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 11, no. 2, p. 366, Jul. 2023, doi: 10.26418/justin.v11i2.57449.
M. D. Purnama and A. Sofro, “Pemodelan Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia Jawa Timur Dengan Regresi Logistik Ordinal,” MATHunesa: Jurnal Ilmiah Matematika, vol. 12, no. 3, pp. 654–661, Jul. 2024, doi: 10.26740/mathunesa.v12n3.p654-661.
N. Satyahadewi, R. Tamtama, H. Perdana, and S. K. Huriyah, “Binary Logistics Regression To Predict The Opportunity Of SNMPTN Graduation In Statistics Study Program Of Tanjungpura University,” Mathline : Jurnal Matematika dan Pendidikan Matematika, vol. 8, no. 1, pp. 17–28, Feb. 2023, doi: 10.31943/mathline.v8i1.269.
O. Y. Wardana, M. Ayub, and A. Widjaja, “Perbandingan Akurasi Model Pembelajaran Mesin untuk Prediksi Seleksi Masuk Perguruan Tinggi Negeri,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 1, Apr. 2023, doi: 10.28932/jutisi.v9i1.6126.
E. Roflin, F. Riana, E. Munarsih, Pariana, and I. A. Liberty, Regresi Logistik Biner dan Multinomial . Pekalongan: PT. Nasya Expending Management, 2023.
A. Ermillian and K. Nugroho, “Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 9, no. 3, pp. 281–295, Dec. 2024, doi: 10.30591/jpit.v9i3.8007.
R. herina Situngkir and P. Sembiring, “Analisis Regresi Logistik untuk Menentukan Faktor-Faktor yang Mempengaruhi Kesejahteraan Masyarakat Kabupaten/Kota di Pulau Nias,” FARABI: Jurnal Matematika dan Pendidikan Matematika, vol. 6, no. 1, pp. 25–31, May 2023, doi: 10.47662/farabi.v6i1.432.
L. A. Mukaromah and M. R. Atsani, “Penerapan Metode Bisection Dan Newton-Raphson Untuk Penyelesaian Akar Persamaan Non-Linier Menggunakan Matlab,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 4, no. 2, 2024.
N. Rohmawati, I. S. N. Inayah, and A. Wibowo, “Perbandingan Penggunaan Pythondan Exceldalam Menyelesaikan Persamaan Tak Linier Metode Newton Raphson,” Numerical: Jurnal Matematika dan Pendidikan Matematika, vol. 9, no. 1, 2025.
S. Rahman et al., Python: Dasar dan Pemrograman Berorientasi Objek. Tahta Media Group, 2023.
H. Anugrah, V. T. Pasau, Jufri, and Asran, “Analisis Faktor Yang Mempengaruhi Pencapaian Akreditasi Unggul Pada Program Studi Teknik Informatika Menggunakan Regresi Logistik,” Jurnal DIPAKOMTI, vol. 17, no. 2, 2026.
