Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee

Asriyanik; Agung  Pambudi

doi:10.34306/conferenceseries.v4i1.625

Authors

Asriyanik Muhammadiyah University of Sukabumi
Agung Pambudi Muhammadiyah University of Sukabumi

DOI:

https://doi.org/10.34306/conferenceseries.v4i1.625

Keywords:

Classification, Awardee, Decision Tree, Support Vector Machine

Abstract

Scholarship selection process has specific rules, but if the number of applicants exceeds the quota, a selection process is needed. Based on the observation of a university in Sukabumi, the selection for KIP scholarship has not yet had a standard method. Several methods can be used to assist the selection process, such as classification based on historical data of applicants. The algorithms used for classification include Decision Tree (DT) and Support Vector Machine (SVM). The research process uses SEMMA (Sample, Explore, Modify, Model, Assess) method. Dataset for KIP scholarship awardee from 2021-2022 consist of 519 samples with 16 attributes. From the exploration results, the most important features for model modeling are Status DTKS, Status P3KE, Father's income, mother's income, combined income, and performance. These attributes are converted into numerical data to facilitate model fitting. The K-Fold Cross-Validation results for the Decision Tree model in the case of KIP Scholarship classification yield an accuracy of 78.44% for the entire test dataset, a precision of 0.73107, indicating that 73.11% of the predictions are true, a recall (sensitivity) of 78.45%, and an F1 score of 73.20%. The results for the SVM model are an accuracy of 80.17%, a precision of 84.44%, and a recall of 80.17%.

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