PENINGKATAN ALGORITMA DECISION TREE DALAM MENGKLASIFIKASI TINGKAT KELULUSAN MAHASISWA UNIVERSITAS JAMBI DENGAN SELEKSI FITUR CHI-SQUARE
DOI:
https://doi.org/10.33998/jms.2025.5.2.2331Keywords:
Data Mining, Machine Learning, Classification, Decision Tree, Chi-SquareAbstract
The increasing number of students at Universitas Jambi each year is not accompanied by a proportional graduation rate, making it necessary to analyze student data to provide strategic solutions. This study aims to apply data mining techniques to student data to assist academic advisors in predicting students' graduation status and providing early warnings to help students complete their studies on time, thereby minimizing graduation delays. The data used in this study consists of alumni records from the Faculty of Science and Technology at Universitas Jambi from 2019 to 2024, which have undergone a data cleaning process. The classification method used is the Decision Tree algorithm with Chi-Square feature selection to enhance model accuracy. The results indicate that applying feature selection to the Decision Tree algorithm improves the accuracy to 80.00%, compared to 78.57% without feature selection. Additionally, the precision increased from 86.82% to 84.41%, recall improved from 86.72% to 92.24%, and F1-score rose from 86.77% to 88.13%. These findings suggest that feature selection significantly contributes to enhancing the classification model’s performance in predicting student graduation at Universitas Jambi, particularly by improving recall, which reflects the model’s ability to more accurately identify students who graduate on time.
Downloads
References
B. D. Ginting, L. Arliana, and N. Kadim, “Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Usaha Masyarakat Berdasarkan Jenis Izin Usaha,” vol. 2, no. 4, 2024.
A. I. Saputra, H. Oktavianto, H. Azizah, and A. Faruq, “Penerapan Algoritma Modified K-Nearest Neighbour (MKNN) Pada Klasifikasi Masa Studi Mahasiswa Teknik Informatika Application of the Modified K-Nearest Neighbour (MKNN) AlgorithmIn the Classification of the Study Period of Informatics Engineering Student,” J. Smart Teknol., vol. 3, no. 1, pp. 2774–1702, 2021, [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JST
M. R. Qisthiano, P. A. Prayesy, and I. Ruswita, “Penerapan Algoritma Decision Tree dalam Klasifikasi Data Prediksi Kelulusan Mahasiswa,” G-Tech J. Teknol. Terap., vol. 7, no. 1, pp. 21–28, 2023, doi: 10.33379/gtech.v7i1.1850.
N. Nailil Amani, M. Martanto, and U. Hayati, “Penggunaan Algoritma Decision Tree Untuk Prediksi Prestasi Siswa Di Sekolah Dasar Negeri 3 Bayalangu Kidul,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 473–479, 2024, doi: 10.36040/jati.v8i1.8355.
E. Karyadiputra and Z. Zaenuddin, “Penerapan Algoritma Decision Tree C4.5 Berbasis Seleksi Atribut Chi Squared Untuk Klasifikasi Tingkat Pengetahuan Ibu Dalam Pemberian Asi Eksklusif Pada Bayi,” Technol. J. Ilm., vol. 11, no. 1, p. 7, 2020, doi: 10.31602/tji.v11i1.2685.
O. P. Moerdyanto and I. K. D. Nuryana, “Prediksi Kelulusan Tepat Waktu Menggunakan Pendekatan Pohon Keputusan Algoritma Decision Tree,” J. Informatics Comput. Sci., vol. 05, no. 1, pp. 90–96, 2023, [Online]. Available: https://ejournal.unesa.ac.id/index.php/jinacs/article/view/55329
A. Saputra, T. Azhima, and Y. Siswa, “DALAM MODEL KASUS PREDIKSI KETERLAMBATAN BIAYA KULIAH,” J. Inform. dan Komputer), vol. 6, no. 2, pp. 231–241, 2022.
M. Darmaputra and D. S. Nugroho, “STUDENT GRADUATION PREDICTION USING ALGORITMA K- MEANS WITH FITUR SELECTION CHI SQUARE,” Equilibria Pendidilan, vol. 6, no. 2, pp. 45–48, 2017, [Online]. Available: http://journal.upgris.ac.id/index.php/equilibriapendidikan
V. R. Joseph, “Optimal ratio for data splitting,” Stat. Anal. Data Min., vol. 15, no. 4, pp. 531–538, 2022, doi: 10.1002/sam.11583.
Sriani, Aidil Halim Lubis, and Sofiah, “Classification of Patient Satisfaction Level on Health Services Using the C4.5 Algorithm,” J. Ris. Inform., vol. 6, no. 2, pp. 95–102, 2024, doi: 10.34288/jri.v6i2.283.
E. Andriyani, W., Matematika Pada Kecerdasan Buatan. CV Tohar Media, 2019.
T. Ernayanti, M. Mustafid, A. Rusgiyono, and A. R. Hakim, “Penggunaan Seleksi Fitur Chi-Square Dan Algoritma Multinomial Naïve Bayes Untuk Analisis Sentimen Pelangggan Tokopedia,” J. Gaussian, vol. 11, no. 4, pp. 562–571, 2023, doi: 10.14710/j.gauss.11.4.562-571.
H. Hamria and H. Hamsir, “K-Nearest Neighbor Berbasis Seleksi Atribut Chi Square Untuk Klasifikasi Penerima Beasiswa,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 14, no. 1, pp. 1–10, 2023, doi: 10.24176/simet.v14i1.9178.
H. M. Lumbantobing, R. A. Marcellino, and I. C. Bu’ulolo, “Penerapan Metode Feature Selection pada Algoritma Naïve Bayes dalam Kasus Keyword Extraction,” Citee, pp. 117–123, 2020.
H. Rifa’i, Ryan Hamonangan, Dian Ade Kurnia, Kaslani, and Mulyawan, “Implementasi Algoritma Decision Tree Dalam Klasifikasi Kompetensi Siswa,” KOPERTIP J. Ilm. Manaj. Inform. dan Komput., vol. 6, no. 1, pp. 15–20, 2022, doi: 10.32485/kopertip.v6i1.131.
Endang Etriyanti, “Perbandingan Tingkat Akurasi Metode Knn Dan Decision Tree Dalam Memprediksi Lama Studi Mahasiswa,” J. Ilm. Bin. STMIK Bina Nusant. Jaya Lubuklinggau, vol. 3, no. 1, pp. 6–14, 2021, doi: 10.52303/jb.v3i1.40.
Y. Crismayella, N. Satyahadewi, and H. Perdana, “Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa,” Jambura J. Math., vol. 5, no. 2, pp. 278–288, 2023, doi: 10.34312/jjom.v5i2.18790.
C. N. Dengen, K. Kusrini, and E. T. Luthfi, “Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” Sisfotenika, vol. 10, no. 1, p. 1, 2020, doi: 10.30700/jst.v10i1.484.
