Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost
DOI:
https://doi.org/10.32736/sisfokom.v10i1.939Keywords:
Classification, Adaboost, Ensemble Learning, EnrollmentAbstract
Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately. Making a machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics: graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.References
R. K. Niswatin, “Sistem Seleksi Penerimaan Mahasiswa Baru Menggunakan Metode Weighted Product (WP),” Seminar Nasional Teknologi Informasi dan Multimedia, 2016.
I. S. Melati, L. Linawati, and I. A. D. Giriantari, “Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa,” JTE, vol. 17, no. 3, p. 325, Dec. 2018, doi: 10.24843/MITE.2018.v17i03.P04.
P. Saini and A. Kumar Jain, “Prediction using Classification Technique for the Students' Enrollment Process in Higher Educational Institutions,” IJCA, vol. 84, no. 14, pp. 37–41, Dec. 2013, doi: 10.5120/14646-2966.
S. K. Wanjau and G. M. Muketha, “Improving Student Enrollment Prediction Using Ensemble Classifiers,” IJCATR, vol. 07, no. 03, pp. 122–128, Mar. 2018, doi: 10.7753/IJCATR0703.1003.
N. L. Ab Ghani, Z. Che Cob, S. Mohd Drus, and H. Sulaiman, “Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach,” in Proceedings of the 3rd International Symposium of Information and Internet Technology (SYMINTECH 2018), vol. 565, M. A. Othman, M. Z. A. Abd Aziz, M. S. Md Saat, and M. H. Misran, Eds. Cham: Springer International Publishing, 2019, pp. 43–52.
N. Yahya and A. Jananto, “Komparasi Kinerja Algoritma C 45 Dan Naive Bayes Untuk Prediksi Kegiatan Penerimaanmahasiswa Baru (Studi Kasus Universitas Stikubank Semarang),” p. 8, 2019..
F. Khan, J. Ahamed, S. Kadry, and L. K. Ramasamy, “Detecting malicious URLs using binary classification through adaboost algorithm,” IJECE, vol. 10, no. 1, p. 997, Feb. 2020, doi: 10.11591/ijece.v10i1.pp997-1005.
A. N. Rais and A. Subekti, “Integrasi SMOTE Dan Ensemble AdaBoost Untuk Mengatasi Imbalance Class Pada Data Bank Direct Marketing,” JI. Jurnal. Informatika, vol. 6, no. 2, pp. 278–285, Sep. 2019, doi: 10.31311/ji.v6i2.6186.
S. K. Wanjau and G. M. Muketha, “Improving Student Enrollment Prediction Using Ensemble Classifiers,” IJCATR, vol. 07, no. 03, pp. 122–128, Mar. 2018, doi: 10.7753/IJCATR0703.1003.
I. S. Melati, L. Linawati, and I. A. D. Giriantari, “Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa,” JTE, vol. 17, no. 3, p. 325, Dec. 2018, doi: 10.24843/MITE.2018.v17i03.P04.
Aradea, Satriyo A., Ariyan Z., and Yuliana A., “Penerapan Decision Tree Untuk Penentuan Pola Data Penerimaan Mahasiswa Baru,” Jurnal Penelitian Sitrotika, 2011.
A. F. Rozi, “Sistem Pendukung Keputusan Seleksi Penerimaan Calon Siswa/i Baru Menggunakan Algoritma C4.5 (Studi Kasus: SDIT An-Najah Jatinom Klaten),” teknoin, vol. 21, no. 1, Feb. 2015, doi: 10.20885/teknoin.vol21.iss1.art2.
Hoiriyah, “Algoritma C4.5 Berbasis Seleksi Atribut Menentukan Kemungkinan Pengunduran Diri Mahasiswa,” vol. 9, p. 9, 2018.
A. Nisa and E. Darwiyanto, “Analisis Sentimen Menggunakan Naive Bayes Classifier dengan Chi-Square Feature Selection Terhadap Penyedia Layanan Telekomunikasi,” p. 10, 2019.
S. Amornsamankul, B. Pimpunchat, W. Triampo, J. Charoenpong, and N. Nuttavut, “A Comparison of Machine Learning Algorithms and Their Applications,” International journal of simulation: systems, science & technology, Aug. 2019, doi: 10.5013/IJSSST.a.20.04.08.
Y. Sasaki, “The truth of the F-measure,” p. 5, Oct. 2007.
A. K. Srivastava, “Comparison Analysis of Machine Learning algorithms for Steel Plate Fault Detection,” vol. 06, no. 05, p. 4, 2019.
Saiprasath G., Naren Babu R., ArunPriyan J., Vinayakumar R., Sowmya V., and Soman K. P., “Performance Comparison Of Machine Learning Algorithms For Malaria Detection Using Microscopic Images” IJRAR, 2019.
A. A. Saa, M. Al-Emran, and K. Shaalan, “Mining Student Information System Records to Predict Students’ Academic Performance,” in The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019), vol. 921, A. E. Hassanien, A. T. Azar, T. Gaber, R. Bhatnagar, and M. F. Tolba, Eds. Cham: Springer International Publishing, 2018, pp. 229–239.
W. Handoko, “Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Single Exponential Smoothing (Studi Kasus Amik Royal Kisaran),” Jurnal Teknologi dan Sistem Informasi, no. 2, p. 8, 2019.
R. Vallat, “Pingouin: statistics in Python”. Journal of Open Source Software, vol. 3, 2018, doi: https://doi.org/10.21105/joss.01026
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, 2011, pp . 2825–2830.
A. Yusuf and J. A. R. Hakim, “Pengembangan Perangkat Lunak Prediktor Nilai Mahasiswa Menggunakan Metode Spectral Clustering dan Bagging Regresi Linier,” vol. 1, p. 5, 2012.
Downloads
Published
Issue
Section
License
The copyright of the article that accepted for publication shall be assigned to Jurnal Sisfokom (Sistem Informasi dan Komputer) and LPPM ISB Atma Luhur as the publisher of the journal. Copyright includes the right to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Sisfokom (Sistem Informasi dan Komputer), LPPM ISB Atma Luhur, and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Sisfokom (Sistem Informasi dan Komputer) are the sole and exclusive responsibility of their respective authors.
Jurnal Sisfokom (Sistem Informasi dan Komputer) has full publishing rights to the published articles. Authors are allowed to distribute articles that have been published by sharing the link or DOI of the article. Authors are allowed to use their articles for legal purposes deemed necessary without the written permission of the journal with the initial publication notification from the Jurnal Sisfokom (Sistem Informasi dan Komputer).
The Copyright Transfer Form can be downloaded [Copyright Transfer Form Jurnal Sisfokom (Sistem Informasi dan Komputer).
This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s). After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted. The copyright form should be signed originally, and send it to the Editorial in the form of scanned document to sisfokom@atmaluhur.ac.id.
