Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa
DOI:
https://doi.org/10.30865/mib.v6i4.4572Keywords:
Support Vector Machine Linear, Graduation, StudentAbstract
The accumulation of student databases can occur if students are unable to complete their studies, namely graduating at a predetermined time. Data mining techniques are often used to process student data so that they can produce predictions of student graduation in order to graduate at a predetermined time. One of the data mining techniques that is often used is the Support Vector Machine (SVM) algorithm. This study aims to analyze the performance of the SVM algorithm to produce a predictive model of student graduation in order to graduate at a predetermined time in the Public Health Study Program, Faculty of Public Health, Deli Husada Health Institute. The method used in this study is a linear SVM algorithm starting from data retrieval by selecting the attributes that will be used for the next stage, data processing consists of cleaning data whose contents do not exist and data transformation which is the determination of the category of each data, modeling is done with the SVM algorithm. from training data and testing and evaluation data to validate and measure the accuracy of the model. The test results with the amount of training data as much as 70% and testing data as much as 30% shows that the linear SVM algorithm provides an accuracy value of 90%References
“20. UU No.12 Tahun 2012 tentang Pendidikan Tinggi.pdf.â€
“PERATURAN MENTERI PENDIDIKAN DAN KEBUDAYAAN no 49 tahun 2014.pdf.â€
N. Mayasari, “Comparison of Support Vector Machine and Decision Tree in Predicting On-Time Graduation,†vol. 02, no. 12, p. 13, 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, doi: 10.14445/22315381/IJETT-V38P268.
“Aplikasi Data Mining dengan Metode Support Vector.pdf.â€
“Predicting students’ academic performance using a modified KNN algorithm.pdf.â€
C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A Practical Guide to Support Vector Classiï¬cation,†p. 16.
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, 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, doi: 10.5815/ijmecs.2019.07.01.
L. W. Santoso and Y. Yulia, “Predicting student performance in higher education using multi-regression models,†TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 18, no. 3, p. 1354, Jun. 2020, doi: 10.12928/telkomnika.v18i3.14802.
A. Kesumawati and D. T. Utari, “Predicting patterns of student graduation rates using Naïve bayes classifier and support vector machine,†East Java, Indonesia, 2018, p. 060005. doi: 10.1063/1.5062769.
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, doi: 10.22487/2540766X.2018.v15.i2.11361.
S. Wiyono, D. S. Wibowo, M. F. Hidayatullah, and D. Dairoh, “Comparative Study of KNN, SVM and Decision Tree Algorithm for Student’s Performance Prediction,†Int. J. Comput. Sci. Appl. Math., vol. 6, no. 2, p. 50, Aug. 2020, doi: 10.12962/j24775401.v6i2.4360.
“Prediction of Student Graduation Time Using The Best Algorithm.pdf.â€
A. S. Nugroho, A. B. Witarto, and D. Handoko, “–Teori dan Aplikasinya dalam Bioinformatika1–,†p. 11, 2003.
A. A. Saa, “Educational Data Mining & Students’ Performance Prediction,†Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 5, p. 9, 2016.
N. Naicker, T. Adeliyi, and J. Wing, “Linear Support Vector Machines for Prediction of Student Performance in School-Based Education,†Math. Probl. Eng., vol. 2020, pp. 1–7, Oct. 2020, doi: 10.1155/2020/4761468.
S. A. Faraby, “Analisis Dan Implementasi Support Vector Machine Dengan String Kernel Dalam Melakukan Klasifikasi Berita Berbahasa Indonesia,†p. 10.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
