Comparison of Naïve Bayes and C4.5 Algorithms in Classifying the Eligibility of Smart Indonesia Card (KIP) College Scholarship Recipients

Dary Mochamad Rifqie; Ahmad Faris Al Faruq; Muh Alif; Rendy Hidayat

doi:10.61255/pisces.v4i1.1807

Authors

Dary Mochamad Rifqie Universitas Negeri Makassar
Ahmad Faris Al Faruq Universitas Negeri Makassar
Muh Alif Universitas Negeri Makassar
Rendy Hidayat Universitas Negeri Makassar

DOI:

https://doi.org/10.61255/pisces.v4i1.1807

Keywords:

C4.5, Classification, Data Mining, KIP Kuliah, Naïve Bayes

Abstract

The Smart Indonesia Card for College Students (KIP Kuliah) program aims to expand access to higher education for students from economically disadvantaged families. However, the scholarship recipient selection process in several institutions still faces challenges because it is conducted manually, requiring considerable time and potentially resulting in inaccurate targeting of eligible recipients. This condition may cause students who meet the eligibility criteria to remain unidentified. This study aims to compare the performance of the Naïve Bayes and Decision Tree C4.5 algorithms in classifying the eligibility of KIP Kuliah scholarship recipients based on student criteria data. The study used a dataset consisting of 76 samples with 8 independent attributes. The analysis was conducted using the Orange application, with 80% of the data used for training and 20% for testing. The performance of both algorithms was evaluated using Area Under the Curve (AUC), Classification Accuracy (CA), F1-Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The results showed that Naïve Bayes achieved better classification performance than C4.5, with an AUC of 0.857, CA of 0.688, F1-Score of 0.686, and MCC of 0.378. In comparison, C4.5 achieved an AUC of 0.469, CA of 0.500, and MCC of 0.000. These findings indicate that Naïve Bayes is more suitable for classifying the eligibility of KIP Kuliah scholarship recipients within the dataset used in this study and has the potential to support a more efficient and accurately targeted selection process.

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