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.
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