Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree

Ade Ermillian; Kristiawan Nugroho

doi:10.30591/jpit.v9i3.8007

Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree

Ade Ermillian, Kristiawan Nugroho

Abstract

The phenomenon of student dropouts is one of the main challenges in education, influenced by various factors such as absenteeism, economic pressures on families, low academic performance, and lack of motivation. This issue not only affects the personal development of students but also tarnishes the reputation of educational institutions. Therefore, an innovative technology-based approach, such as data mining, is needed to detect students at risk of dropping out early. This study aims to design a model for detecting the potential of school dropout students using Logistic Regression and Decision Tree methods based on student data from SMA N 4 Tegal. The variables used in the analysis include demographic, academic, and social information such as absenteeism, average semester grades, parental income, and transportation type. The dataset is processed using one-hot encoding and label encoding techniques to convert categorical data into numeric values. The results indicate that both methods have their respective advantages. The Decision Tree model achieves high precision, especially in predicting students who continue their education, with a precision of 0.99 for the "Continue School" class. However, recall for the "Dropout" class remains low (0.60), indicating the need for improvements in detecting students at risk of dropping out. On the other hand, the Logistic Regression model shows better balance in detecting both classes, with more balanced accuracy and recall. This study concludes that both models can be used to monitor the potential of school dropouts and provide data-driven recommendations for more accurate educational decision-making.

Keywords

Logistic Regression, Decision Tree, student dropout, risk detection, data analysis

Full Text:

References

N. Shiratori, “Derivation of Student Patterns in a Preliminary Dropout State and Identification of Measures for Reducing Student Dropouts,” in Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018, Institute of Electrical and Electronics Engineers Inc., Jul. 2018, pp. 497–500. doi: 10.1109/IIAI-AAI.2018.00108.

Debora; R. M. et al., “PENERAPAN ALGORITME C.45 UNTUK KLASIFIKASI MAHASISWA BERPOTENSI DROP OUT PADA UNIVERSITAS BUDI LUHUR,” SENAFTI, vol. 2, no. 1, pp. 316–325, Apr. 2023.

Sanjaya; D. et al., “Penerapan Data Mining untuk Prediksi Mahasiswa Berpotensi Non-Aktif Menggunakan Algoritma C4.5: Studi Kasus STMIK Primakara,” Jurnal Ilmiah Ilmu Terapan Universitas Jambi, vol. 6, no. 1, pp. 84–97, Jun. 2022.

Agus Budiyantara et al., “KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MEMPREDIKSI MAHASISWA LULUS TEPAT WAKTU,” JURNAL ILMU PENGETAHUAN DAN TEKNOLOGI KOMPUTER, vol. 5, pp. 265–270, Feb. 2020.

E. ; Osmanbegovic and M. Suljic, “Data Mining Approach for Predicting Student Performance,” 2012. [Online]. Available: https://hdl.handle.net/10419/193806

K. O. T. U. Otgontsetseg Sukhbaatar, “Mining Educational Data to Predict Academic Dropouts: a Case Study in Blended Learning Course,” Proceedings of TENCON, vol. 10, pp. 2205–2208, Oct. 2018.

R. Amalia, “Penerapan Data Mining untuk Memprediksi Hasil Kelulusan Siswa Menggunakan Metode Naïve Bayes,” JUISI, vol. 06, no. 01, 2020.

P. B. Mayank Pareek, “A REVIEW REPORT ON KNOWLEDGE DISCOVERY IN DATABASES AND VARIOUS TECHNIQUES OF DATA MINING,” OAIJSE, vol. 5, no. 12, pp. 79–82, Dec. 2020.

M. Nurizki, W. Apriandari, and A. Asriyanik, “Algoritma Naïve Bayes untuk Rekomendasi Seleksi Peserta Paskibraka Berbasis Website,” Journal of Information System Research (JOSH), vol. 4, no. 4, pp. 1486–1493, Jul. 2023, doi: 10.47065/josh.v4i4.3574.

M. Atalya, A. Leza, W. Utami, P. Anugrah, and C. Dewi, “PREDIKSI PRESTASI SISWA SMAS KATOLIK SANTO YOSEPH DENPASAR BERDASARKAN KEDISIPLINAN DAN TINGKAT EKONOMI ORANG TUA MENGGUNAKAN METODE KNOWLEDGE DISCOVERY IN DATABASE DAN ALGORITMA REGRESI LINIER BERGANDA,” 2024.

A. N. Putri, N. Wakhidah, and V. G. Utomo, “Pemanfaatan Data Mining untuk Media Pembelajaran di SMK Hidayah Semarang,” Jurnal Pengabdian kepada Masyarakat, vol. 13, no. 3, pp. 487–491, [Online]. Available: http://journal.upgris.ac.id/index.php/e-dimas

D. Yuniarti and dan Rito Goejantoro, “Perbandingan Metode Klasifikasi Regresi Logistik Dengan Jaringan Saraf Tiruan (Studi Kasus: Pemilihan Jurusan Bahasa dan IPS pada SMAN 2 Samarinda Tahun Ajaran 2011/2012) Comparison of Classification Methods Between Logistic Regression and Artificial Neural Network (Case Study: Selection of Language and Social Studies Depertement at SMAN 2 Samarinda academic year 2011/2012),” Jurnal EKSPONENSIAL, vol. 4, no. 1, 2013.

Y.- Brahmantyo, R. Riaman, and F. Sukono, “Willingness to Pay of Fishermen Insurance Using Logistic Regression with Parameter Estimated by Maximum Likelihood Estimation Based on Newton Raphson Iteration,” Jurnal Matematika Integratif, vol. 17, no. 1, p. 15, Aug. 2021, doi: 10.24198/jmi.v17.n1.32037.15-21.

M. J. Aitkenhead, “A co-evolving decision tree classification method,” Expert Syst Appl, vol. 34, no. 1, pp. 18–25, Jan. 2008, doi: 10.1016/j.eswa.2006.08.008.

B. Aviad and G. Roy, “Classification by clustering decision tree-like classifier based on adjusted clusters,” Expert Syst Appl, vol. 38, no. 7, pp. 8220–8228, Jul. 2011, doi: 10.1016/j.eswa.2011.01.001.

Sugianto C. A., “PENERAPAN TEKNIK DATA MINING UNTUK MENENTUKAN HASIL SELEKSI MASUK SMAN 1 GIBEBER UNTUK SISWA BARU MENGGUNAKAN DECISION TREE,” TEDC, vol. 9, no. 1, pp. 39–43, Jan. 2015.

W. Yustanti, “Studi Komparasi Local Outlier Factor (LOF) dan Isolation Forest (IF) pada Analisis Anomali Kinerja Dosen,” Journal of Informatics and Computer Science, vol. 06, 2024.

DOI: https://doi.org/10.30591/jpit.v9i3.8007