Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu

  • Emy Haryatmi Universitas Gunadarma
  • Sheila Pramita Hervianti Universitas Gunadarma
Keywords: SVM, graduation, students, algorithm, machine learning

Abstract

A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time.

 

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References

N. Mayasari, “Comparison of Support Vector Machine and Decision Tree in Predicting On-Time Graduation (Case Study : Universitas Pembangunan Panca Budi),” Int. J. Recent Trends Eng. Res., vol. 2, no. 12, pp. 140–151, Dec. 2016.

L. Marlina, M. lim, and A. P. Utama Siahaan, “Data Mining Classification Comparison (Naïve Bayes and C4.5 Algorithms),” Int. J. Eng. Trends Technol., vol. 38, no. 7, pp. 380–383, Aug. 2016.

N. Mohammad Suhaimi, S. Abdul-Rahman, S. Mutalib, N. H. Abdul Hamid, and A. Hamid, “Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms,” Int. J. Mod. Educ. Comput. Sci., vol. 11, no. 7, pp. 1–13, Jul. 2019.

A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi Algoritme Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 4, pp. 1704–1708, Mar. 2018.

T. Handhayani and L. Hiryanto, “Predicting And Analyzing The Student’s Length of Studi-Time Using Support Vector Machine,” ComTech Comput. Math. Eng. Appl., vol. 8, no. 2, pp. 107–114, Mar. 2017.

A. Saifudin, “Metode Data Mining Untuk Seleksi Calon Mahasiswa Pada Penerimaan Mahasiswa Baru di Universitas Pamulang,” J. Teknol., vol. 10, no. 1, pp. 25–36, Jul. 2018.

A. Kesumawati and D. T. Utari, “Predicting Patterns of Student Graduation Rates Using Naïve Bayes Classifier and Support Vector Machine,” AIP Conf. Proc., vol. 2021, no. 1, pp. 060005-1-060005–10, Oct. 2018.

A. Fadli, M. I. Zulfa, and Y. Ramadhani, “Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness,” J. Teknol. dan Sist. Komput., vol. 6, no. 4, pp. 158–163, Oct. 2018.

I. T. Utami, “Perbandingan Kinerja Klasifikasi Support Vector Machine (SVM) Dan Regresi Logistik Biner Dalam Mengklasifikasikan Ketepatan Waktu Kelulusan Mahasiswa Fmipa Untad,” J. Ilm. Mat. Dan Terap., vol. 15, no. 2, pp. 256–267, Dec. 2018.

S. Wiyono and T. Abidin, “Comparative Study of Machine Learning KNN, SVM, and Decision Tree Algorithm To Predict Student’s Performance,” Int. J. Res. -GRANTHAALAYAH, vol. 7, no. 1, pp. 190–196, Jan. 2019.

V. Riyanto, A. Hamid, and R. Ridwansyah, “Prediction of Student Graduation Time Using the Best Algorithm,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 1, pp. 1–9, Mar. 2019.

R. A. Permana and S. Sahara, “Metode Support Vector Machine Sebagai Penentu Kelulusan Mahasiswa pada Pembelajaran Elektronik,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 50–58, Jun. 2019.

Published
2021-04-29
How to Cite
Haryatmi, E., & Pramita Hervianti, S. (2021). Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 386 - 392. https://doi.org/10.29207/resti.v5i2.3007
Section
Artikel Teknologi Informasi