Classification of Student Graduation Using Learning Vector Quantization Algorithm
DOI:
https://doi.org/10.31961/positif.v3i2.420Keywords:
Classification, Graduation, Learning Vector Quantization, Artificial Neural NetworkAbstract
Computer Science Study Program FMIPA ULM graduates dozens of undergraduate students every year. One of the assessment criteria for the accreditation of the study program is the assessment of the duration of the study of students who graduated on time. In this research will be done classification of graduation based on the status of student study year = timely and study length 4.5 years = not on time. Classification of students passing graduation based on IP semester I, Semester II, Semester III and Semester IV that have passed. If a system can classify students' graduation as a predictor of the duration of a student study, it is expected to be a recommendation for the Academic Advisors lecturers giving advice to students who are detected in the timely graduation possibilities so that Drop Out (DO) prevention measures may be taken earlier. Accuracy results are in accordance with the test data of 70% by using α = 0.5, decrement alfa 0.35 and maxepoch = 500.
Downloads
References
Hermawan, Arief. (2006). Jaringan Saraf Tiruan Teori dan Aplikasi. Yogyakarta : Andi Offset.
Hidayati, N. & Warsito, B. (2010). Prediksi Terjangkitnya Penyakit Jantung Dengan Metode Learning Vector Quantization. Media Statistika, 3 (1). pp. 21-30. ISSN 1979-3693.
Kusumawati, D., Winarno, W. W., & Arief, M. R. (2015). Prediksi Kelulusan Mahasiswa Menggunakan Metode Neural Network dan Particle Swarm Optimization. SEMNASTEKNOMEDIA ONLINE, 3(1), 3-8.
Affendey, L. S., Paris, I. H. M., Mustapha, N., Sulaiman, M. N., & Muda, Z. (2010). Ranking of influencing factors in predicting students’ academic performance. Information Technology Journal, 9(4), 832-837.
Rahmani, B., & Aprilianto, H. (2014). Early Model of Student's Graduation Prediction Based on Neural Network. TELKOMNIKA (Telecommunication Computing Electronics and Control), 12(2), 465-474.
Siang, J. J., Tiruan, J. S., & menggunakan MATLAB, P. (2004). Yogyakarta. Andi Offset.
Ranadhi, D., Indarto, W., & Hidayat, T. (2006). Implementasi Learning Vector Quantization (LVQ) untuk Pengenal Pola Sidik Jari pada Sistem Informasi Narapidana LP Wirogunan. Media Informatika, 4(1).
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with Positif : Jurnal Sistem dan Teknologi Informasi agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement 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 acknowledgement 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.
This work is licensed under a Creative Commons Attribution 4.0 International License.