Comparative Analysis of Data Mining Classification Algorithm Performance for Searching Prospective Student Interests

Budiman Budiman, Zatin Niqotaini

Abstract


Admission of new students is an activity that’s always carried out by every university in the new academic year. The decline in the number of registrants every year is an obstacle for AMIK HASS in new student admissions, efforts are needed to process the existing data on new student admissions. Data mining applications use classification algorithms that aim to make predictions. The algorithms used are Nave Bayes (NB), Decision Tree J48 (J48), K-Nearest Neighbor (K-NN), Random Forest (RF), and Support Vector Machine (SVM). Algorithm testing to analyze the performance of each algorithm uses WEKA. The data set used in this study is the search for potential and interest of prospective new students as many as 5,934 records. The tests carried out on the five algorithms use the test percentage split mode, which is 70% for training data and 30% for test data. The highest accuracy rate on J48 is 90.34% followed by RF at 89.04%, SVM at 88.43%, K-NN at 87.53%, NB at 87.25%. J48 is the best algorithm for testing data sets with the lowest prediction error rate of 0.26. The J48 classification algorithm has explicit rules for the classification and handling of heterogeneous data by having 255 rules.


Full Text:

PDF

References


D. Aribowo and A. E. H. Setiadi, “Analisa Komparasi Algoritma Data Mining untuk Klasifikasi Heregistrasi Calon Mahasiswa STMIK Widya Pratama,” IC-Tech, vol. 13, no. 2, pp. 1–6, Sep. 2018, Accessed: Jul. 29, 2021. [Online]. Available: https://ejournal.stmik-wp.ac.id/index.php/ictech/article/view/30.

Z. Zulfauzi and M. N. Alamsyah, “Penerapan Algoritma Naive Bayes Untuk Prediksi Penerimaan Mahasiswa Baru Studi Kasus Universitas Bina Insan Fakultas Komputer,” J. Teknol. Inf. Mura, vol. 12, no. 02, pp. 156–165, Dec. 2020, doi: 10.32767/JTI.V12I02.1096.

R. K. Niswatin, “Sistem Seleksi Penerimaan Mahasiswa Baru Menggunakan Metode Weighted Product (WP),” in Seminar Nasional Teknologi Informasi dan Multimedia 2016, Feb. 2016, vol. 4, no. 1, pp. 1.3-31, Accessed: Aug. 20, 2021. [Online]. Available: https://ojs.amikom.ac.id/index.php/semnasteknomedia/article/view/1338.

I. Kurniawati, R. E. Indrajit, and M. Fauzi, “Peran Bussines Intelligence Dalam Menentukan Strategi Promosi Penerimaan Mahasiswa Baru,” IKRA-ITH Inform. J. Komput. dan Inform., vol. 1, no. 2, pp. 70–79, 2017, Accessed: Aug. 20, 2021. [Online]. Available: https://journals.upi-yai.ac.id/index.php/ikraith-informatika/article/view/122.

N. Yahya and A. Jananto, “Komparasi Kinerja Algoritma C.45 dan Naive Bayes untuk Prediksi Kegiatan Penerimaan Mahasiswa Baru (Studi Kasus : Universitas STIKUBANK Semarang),” in Prosiding SENDI_U, 2019, pp. 221–228, Accessed: Mar. 08, 2021. [Online]. Available: https://www.unisbank.ac.id/ojs/index.php/sendi_u/article/view/7389.

N. A. Haris, M. Abdullah, N. Hasim, and F. Abdul Rahman, “A study on students enrollment prediction using data mining,” Jan. 2016, doi: 10.1145/2857546.2857592.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques Third Edition, Third Edit. Morgan Kaufmann, 2012.

B. Nakhkob and M. Khademi, “Predicted Increase Enrollment in Higher Education Using Neural Networks and Data Mining Techniques,” J. Adv. Comput. Res., vol. 7, no. 4, pp. 125–140, Nov. 2016, Accessed: Jul. 29, 2021. [Online]. Available: http://jacr.iausari.ac.ir/article_651013.html.

A. P. Dela Cruz et al., “Higher education institution (Hei) enrollment forecasting using data mining technique,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 2, pp. 2060–2064, Mar. 2020, doi: 10.30534/IJATCSE/2020/179922020.

S. K. Wanjau and G. M. Muketha, “Improving Student Enrollment Prediction Using Ensemble Classifiers,” Int. J. Comput. Appl. Technol. Res., vol. 7, no. 3, pp. 122–128, 2018, Accessed: Jul. 29, 2021. [Online]. Available: http://repository.mut.ac.ke/handle/123456789/2998.

S. Shilbayeh and A. Abonamah, “Predicting Student Enrolments and Attrition Patterns in Higher Educational Institutions using Machine Learning,” Int. Arab J. Inf. Technol., vol. 18, no. 4, pp. 562–567, 2021, doi: 10.34028/18/4/8.

M. Mubassiran and M. I. Choldun, “Penerapan Algoritma K-Nearest Neighbors Untuk Menentukan Pola Penerimaan Mahasiswa Baru (Studi Kasus: Politeknik Pos Indonesia),” Improve, vol. 11, no. 2, pp. 1–8, Nov. 2019, Accessed: Jul. 29, 2021. [Online]. Available: https://ejurnal.poltekpos.ac.id/index.php/improve/article/view/746.

I. Loelianto, M. S. S. Thayf, and H. Angriani, “Implementasi Teori Naive Bayes Dalam Klasifikasi Calon Mahasiswa Baru STMIK Kharisma Makassar,” SINTECH (Science Inf. Technol. J., vol. 3, no. 2, pp. 110–117, Oct. 2020, doi: 10.31598/SINTECHJOURNAL.V3I2.651.

K. A. Pratama, G. A. Pradnyana, and I. K. R. Arthana, “Pengembangan Sistem Cerdas Untuk Prediksi Daftar Kembali Mahasiswa Baru Dengan Metode Naive Bayes (Studi Kasus: Universitas Pendidikan Ganesha),” SINTECH (Science Inf. Technol. J., vol. 3, no. 1, pp. 22–34, Apr. 2020, doi: 10.31598/SINTECHJOURNAL.V3I1.523.

M. L. Dalafranka, “Penerapan Algoritma Id3 untuk Mendukung Sosialisasi Penerimaan Mahasiswa Baru yang Tepat Sasaran di UIN Raden Fatah Palembang,” Teknomatika, vol. 10, no. 2, pp. 141–150, Oct. 2020, Accessed: Jul. 29, 2021. [Online]. Available: http://ojs.palcomtech.com/index.php/teknomatika/article/view/501.

A. Handayanto, K. Latifa, N. D. Saputro, and R. R. Waliansyah, “Analisis dan Penerapan Algoritma Support Vector Machine (SVM) dalam Data Mining untuk Menunjang Strategi Promosi,” JUITA J. Inform., vol. 7, no. 2, pp. 71–79, Nov. 2019, doi: 10.30595/JUITA.V7I2.4378.

S. Wulandari and M. Iqbal, “Analisis Kelayakan Lokasi Promosi Dalam Penerimaan Mahasiswa Baru (PMB) Dengan Algoritma Naïve Bayes & Decission Tree C4.5,” KILAT, vol. 10, no. 1, pp. 169–178, Apr. 2021, doi: 10.33322/KILAT.V10I1.1196.

“Weka 3 - Data Mining with Open Source Machine Learning Software in Java.” Waikato University, Accessed: Jul. 31, 2021. [Online]. Available: https://www.cs.waikato.ac.nz/ml/weka/.

D. T. Larose and C. D. Larose, Data Mining and Predictive Analytics. Hoboken, New Jersey: John Wiley & Sons, Inc., 2015.

B. Budiman, R. Nursyanti, R. Y. R. Alamsyah, and I. Akbar, “Data Mining Implementation Using Naïve Bayes Algorithm and Decision Tree J48 In Determining Concentration Selection,” Int. J. Quant. Res. Model., vol. 1, no. 3, pp. 123–134, Sep. 2020, doi: 10.46336/ijqrm.v1i3.72.

M. M. Saritas and A. Yasar, “Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification,” Int. J. Intell. Syst. Appl. Eng., vol. 7, no. 2, pp. 88–91, Jun. 2019, doi: 10.1039/b000000x.

“J48 numbers - Weka Wiki.” https://waikato.github.io/weka-wiki/not_so_faq/j48_numbers/ (accessed Mar. 08, 2021).

L. Breiman, “Random Forests,” Mach. Learn. 2001 451, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.

J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Syst. Appl., vol. 134, pp. 93–101, Nov. 2019, doi: 10.1016/J.ESWA.2019.05.028.

F. Gorunescu, Data Mining: Concepts, Models and Techniques. Berlin: Springer-Verlag Berlin Heidelberg, 2011.




DOI: https://doi.org/10.32520/stmsi.v11i2.1610

Article Metrics

Abstract view : 177 times
PDF - 88 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.