Prediction of PTIK students' study success in the first year using the c4.5 algorithm

Main Article Content

Asri Astuti
Dwi Maryono
Febri Liantoni

Abstract

The purpose of this study is to determine the factors that influence the success of student studies in the first year through data mining research using the C4.5 algorithm. This research is a type of quantitative research. This research uses student data of a study program as much as 85 data which will be processed using the Weka application. The data obtained will then be processed using the C4.5 data mining method to produce a decision tree containing rules to predict the success of student studies in the first year. The best result using percentage-split 80% obtained an accuracy of 82.35% as well as the rules contained in the decision tree. The most important factor in determining the success of studies in first-year students is the selection of college entrance pathways. Other factors that become other determinants are education before college, intensity of communication with friends, class year, intensity of off-campus organizations, and plans to change study programs.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
A. Astuti, D. Maryono, and F. Liantoni, “Prediction of PTIK students’ study success in the first year using the c4.5 algorithm”, J. Soft Comput. Explor., vol. 5, no. 1, pp. 32-37, Mar. 2024.
Section
Articles

References

P. Pampouktsi et al., “Techniques of Applied Machine Learning Being Utilized for the Purpose of Selecting and Placing Human Resources within the Public Sector,” J. Inf. Syst. Explor. Res., vol. 1, no. 1, pp. 1–16, Dec. 2022, doi: 10.52465/joiser.v1i1.91.

F. N. R. F. J. Aziz, B. D. Setiawan, and I. Arwani, “Implementasi Algoritma K-Means untuk Klasterisasi Kinerja Akademik Mahasiswa,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 6 SE-, pp. 2243–2251, Sep. 2017, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1571

A. Y. Lubalu, C. K. Ekowati, and P. A. Udil, “Pengaruh Jalur Seleksi Masuk Universitas Terhadap IPK Tahun Pertama Mahasiswa Angkatan Tahun 2020 Program Studi Pendidikan Matematika FKIP Universitas Nusa Cendana,” Haumeni J. Educ., vol. 2, no. 1, pp. 20–26, May 2022, doi: 10.35508/haumeni.v2i1.7074.

A. Wibowo, D. Manongga, and H. D. Purnomo, “The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation,” Sci. J. Informatics, vol. 7, no. 1, pp. 99–112, May 2020, doi: 10.15294/sji.v7i1.24241.

E. Alhazmi and A. Sheneamer, “Early Predicting of Students Performance in Higher Education,” IEEE Access, vol. 11, pp. 27579–27589, 2023, doi: 10.1109/ACCESS.2023.3250702.

J. J. R. Fanggidae, “Klasifikasi Faktor–faktor yang Mempengaruhi Prestasi Akademik Mahasiswa Pendidikan Matematika FKIP Undana dengan Metode CHAID,” FRAKTAL J. Mat. DAN Pendidik. Mat., vol. 2, no. 1, pp. 23–33, May 2021, doi: 10.35508/fractal.v2i1.4018.

S. Fitri, N. Nurjanah, and W. Astuti, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa (Studi Kasus: Umtas),” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 633–640, Apr. 2018, doi: 10.24176/simet.v9i1.2002.

N. B. Nasution, D. Hartanto, D. J. Silitonga, Lasimin, and D. Mardhiyana, “Prediksi Lama Studi dan Predikat Kelulusan Mahasiswa Menggunakan Algoritma Supervised Learning,” G-Tech J. Teknol. Terap., vol. 7, no. 2, pp. 386–395, Mar. 2023, doi: 10.33379/gtech.v7i2.2077.

A. Azahari, Y. Yulindawati, D. Rosita, and S. Mallala, “Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, pp. 443–452, May 2020, doi: 10.25126/jtiik.2020732093.

Y. E. Fadrial, “Algoritma Naive Bayes Untuk Mencari Perkiraan Waktu Studi Mahasiswa,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 4, no. 1, pp. 20–29, May 2021, doi: 10.31539/intecoms.v4i1.2219.

I. W. Saputro and B. W. Sari, “Uji Performa Algoritma Naïve Bayes untuk Prediksi Masa Studi Mahasiswa,” Creat. Inf. Technol. J., vol. 6, no. 1, p. 1, Apr. 2020, doi: 10.24076/citec.2019v6i1.178.

M. Windarti and A. Suradi, “Perbandingan Kinerja 6 Algoritme Klasifikasi Data Mining untuk Prediksi Masa Studi Mahasiswa,” Telematika, vol. 12, no. 1, p. 14, Feb. 2019, doi: 10.35671/telematika.v12i1.778.

A. F. Mulyana, W. Puspita, and J. Jumanto, “Increased accuracy in predicting student academic performance using random forest classifier,” J. Student Res. Explor., vol. 1, no. 2, pp. 94–103, Jul. 2023, doi: 10.52465/josre.v1i2.169.

A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 1, May 2020, doi: 10.31294/ijcit.v5i1.7951.

X. Wang, C. Zhou, and X. Xu, “Application of C4.5 decision tree for scholarship evaluations,” Procedia Comput. Sci., vol. 151, pp. 179–184, 2019, doi: 10.1016/j.procs.2019.04.027.

H. A. Prihanditya and A. Alamsyah, “The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease,” J. Soft Comput. Explor., vol. 1, no. 1, Sep. 2020, doi: 10.52465/joscex.v1i1.8.

Y. Luvia, D. Hartama, A. Windarto, and S. Solikhun, “Penerapan Algoritma C4.5 Untuk Klasifikasi Predikat Keberhasilan Mahasiswa Di Amik Tunas Bangsa,” Jurasik (Jurnal Ris. Sist. Inf. Tek. Inform., vol. 1, pp. 75–79, Jul. 2016, doi: 10.30645/jurasik.v1i1.12.

Abstract viewed = 88 times