Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa

Yuveinsiana Crismayella; Neva Satyahadewi; Hendra Perdana

doi:10.34312/jjom.v5i2.18790

Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa

Yuveinsiana Crismayella, Neva Satyahadewi, Hendra Perdana

Abstract

Colleges provide higher education as the benchmark of education quality and evaluate higher education syllabi. Graduation rates and enrollment capacity are essential for graduation assessment and decision-making. Unfortunately, some students majoring in statistics failed to finish their studies on time, impacting the accreditation of the study program. It is necessary to examine the characteristics of students who managed and failed to complete their studies on time using the data mining classification method, namely Algorithm C5.0. In this study, Adaboost algorithm and Algorithm C5.0 was employed to classify graduation rates accurately. Graduation data of the Statistics Study Program of Universitas Tanjungpura Batch 1 of 20217/2018 to Batch II of 2022/2023 School years were regarded in this study. First, the entropy, gain, and gain ratio values were measured. After that, each data was given equal weight, and iteration was performed 100 times. The analysis using Algorithm C5.0 showed School Accreditation as the variable with the highest gain ratio, indicating that School Accreditation has the most decisive influence on graduation rates with an accuracy percentage of 70%. This percentage then increased to 82.14% after the boosting using the Adaboost algorithm. Adaboost Algorithm is regarded as good in improving the accuracy of algorithm C5.0. The results of this study can provide insight for colleges in designing policies to increase on-time graduation based on the factors that influence student graduation.

Keywords

Study Period; C5.0 Algorithm; Adaptive Boosting; Adaboost Algorithm

Full Text:

PDF

References

M. Kadafi, “Metode na¨ıve bayes classifier (nbc) untuk memprediksi tingkat kelulusan mahasiswa tepat waktu,” JSI: Jurnal Sistem Informasi (E-Journal), vol. 12, no. 2, pp. 8–17, 2020, doi: 10.36706/jsi.v12i2.12179.

H. Hartatik, “Optimasi model prediksi kelulusan mahasiswa menggunakan algoritma naive bayes,” Indonesian Journal of Applied Informatics, vol. 5, no. 1, p. 32, 2021, doi: 10.20961/ijai.v5i1.44379.

P. B. N. Setio, D. R. S. Saputro, and B. Winarno, “Klasifikasi dengan pohon keputusan berbasis algoritme c4.5.” Universitas negeri Semarang, 2020, pp. 64–71.

M. S. Mustafa, M. R. Ramadhan, and A. P. Thenata, “Implementasi data mining untuk evaluasi kinerja akademik mahasiswa menggunakan algoritma naive bayes classifier,” Creative Information Technology Journal, vol. 4, no. 2, pp. 151–162, 2018, doi:10.24076/citec.2017v4i2.106.

F. N. Umma, B. Warsito, and D. A. I. Maruddani, “Klasifikasi status kemiskinan rumah tangga dengan algoritma c5.0 di kabupaten pemalang,” Jurnal Gaussian, vol. 10, no. 2, pp. 221–229, 2021, doi: 10.14710/j.gauss.v10i2.29934.

Y. Pristyanto, “Penerapan metode ensemble untuk meningkatkan kinerja algoritme klasifikasi pada imbalanced dataset,” Jurnal Teknoinfo, vol. 13, no. 1, pp. 11–16, 2019, doi: 10.33365/jti.v13i1.184.

A. Bisri and R. Rachmatika, “Integrasi gradient boosted trees dengan smote dan bagging untuk deteksi kelulusan mahasiswa,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 8, no. 4, pp. 309–314, 2019, doi: 10.22146/jnteti.v8i4.529.

A. I. Prianti, R. Santoso, and A. R. Hakim, “Perbandingan metode k-nearest neighbor dan adaptive boosting pada kasus klasifikasi multi kelas,” Jurnal Gaussian, vol. 9, no. 3, pp. 346–354, 2020, doi: 10.14710/j.gauss.v9i3.28924.

L. Qadrini, A. Seppewali, and A. Aina, “Decision tree dan adaboost pada klasifikasi penerima program bantuan sosial,” J. Inovasi Penelitian, vol. 2, no. 7, pp. 1959–1966, 2021.

N. Novianti, M. Zarlis, and P. Sihombing, “Penerapan algoritma adaboost untuk peningkatan kinerja klasifikasi data mining pada imbalance dataset diabetes,” Jurnal Media Informatika Budidarma, vol. 6, no. 2, pp. 1200–1206, 2022, doi: 10.30865/mib.v6i2.4017.

W. Wahyudi, “Optimasi klasifikasi status gizi balita berdasarkan indeks antropometri menggunakan algoritma c4.5 adaboost classification,” Jurnal Ilmiah Komputerisasi Akuntansi, vol. 12, no. 2, pp. 45–51, 2019.

I. A. Rahmi, F. M. Afendi, and A. Kurnia, “Metode adaboost dan random forest untuk prediksi peserta jkn-kis yang menunggak,” Jambura Journal of Mathematics, vol. 5, no. 1, pp. 83–94, 2023, doi: 10.34312/jjom.v5i1.15869.

T. Tanti, P. Sirait, and A. Andri, “Optimalisasi kinerja klasifikasi melalui seleksi fitur dan adaboost dalam penanganan ketidakseimbangan kelas,” Jurnal Media Informatika Budidarma, vol. 5, no. 4, pp. 1377–1385, 2021, doi: 10.30865/mib.v5i4.3280.

A. N. Rais, “Integrasi smote dan ensemble adaboost untuk mengatasi imbalance class pada data bank direct marketing,” Jurnal Informatika, vol. 6, no. 2, pp. 278–285, 2019, doi: 10.31311/ji.v6i2.6186.

J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data, vol. 6, no. 1, p. 27, 2019, doi: 10.1186/s40537-019-0192-5.

DOI: https://doi.org/10.34312/jjom.v5i2.18790