Model for identifying high-achieving students using the k-means clustering algorithm and c4.5 classification

Kalfinus  Waruwu; Gunadi  Widi Nurcahyo; Sumijan

doi:10.35134/komtekinfo.v13i2.681

Authors

Kalfinus Waruwu Universitas Putra Indonesia YPTK Padang
Gunadi Widi Nurcahyo Universitas Putra Indonesia YPTK Padang
Sumijan Universitas Putra Indonesia YPTK Padang

DOI:

https://doi.org/10.35134/komtekinfo.v13i2.681

Keywords:

Student Achievement, K-Means Clustering, C4.5 Algorithm, Classification, Decision Tree

Abstract

Student achievement refers to academic accomplishments or results obtained by students in the field of education, which can influence the process of determining student academic grades, class achievement, and accomplishments. This process plays a strategic role in supporting objective educational decision-making, especially in the preparation of coaching programs, the establishment of awards, and the continuous development of student potential. Based on this, the purpose of this study is to analyze data on high-achieving students using the K-Means and C4.5 algorithms. The research methods used include K-means, which functions to group student data into different groups. C4.5 classification is capable of analyzing data characteristic similarities, and the results of the decision tree are used as the basis for the decision-making process. The dataset in this study consisted of 345 students from SMK Negeri 3 Padangsidimpuan. Based on the results of this study, it was proven that the application of the K-Means and C4.5 algorithms could achieve an accuracy of 98.59%. This research contributes to identifying high-achieving students at SMKN 3 Padangsidimpuan using the K-Means and C4.5 algorithms, which can assist the school in formulating more effective and targeted guidance policies and presenting the results of identifying high-achieving students after clustering and decision tree analysis. This serves as a basis for decision-making in determining student development programs based on objectively identified academic and non-academic achievement clusters.

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