Implementation of Support Vector Machine Algorithm for Classification of Study Period and Graduation Predicate of Students

Sumiyatun; Yagus  Cahyadi; Edi Faizal

doi:10.56705/ijodas.v6i1.214

Sumiyatun Universitas Teknologi Digital Indonesia
Yagus Cahyadi Universitas Teknologi Digital Indonesia
Edi Faizal Universitas Teknologi Digital Indonesia

DOI: https://doi.org/10.56705/ijodas.v6i1.214

Keywords: Educational Data, Graduation Predicates Classification, Machine Learning, Study Duration, SVM Algorithm

Abstract

Introduction: Accurately predicting the duration of study and graduation predicates in higher education is essential for improving academic outcomes and decision-making. This study aims to classify students’ study period and graduation predicates in the Information Systems program at UTDI using the Support Vector Machine (SVM) algorithm. Methods: A dataset of 500 student records containing academic and demographic variables—including GPA, age, semesters, and graduation predicates—was processed through data cleaning, normalization, and feature selection. Study duration was categorized into three classes: short (≤4 years), medium (4–6 years), and long (>6 years). An SVM with a linear kernel was applied, and the model was evaluated using accuracy, precision, recall, and F1-score. Results: The SVM model achieved perfect classification for study duration, with 100% accuracy, precision, recall, and F1-score across all categories. For graduation predicate classification, the model attained 95.18% accuracy. While it performed well overall, it faced some difficulty distinguishing between "Cum Laude" and "Very Satisfactory" due to overlapping GPA ranges. The analysis identified GPA as the most influential feature in both classification tasks, while age and the number of semesters played supporting roles. Conclusions: The SVM model demonstrates strong capability in classifying study duration and graduation predicates, offering valuable insights for academic management. Although performance was high, especially for study period prediction, further refinement is suggested to enhance classification in overlapping categories. Future work may benefit from larger, more balanced datasets and exploration of advanced models to increase prediction reliability.

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