Penerapan Metode Quantitative Association Rule untuk Optimalisasi Seleksi Calon Siswa Baru SD IT Seluas Cita Nusantara

Aniq Astofa; Eko Sutono

Authors

Aniq Astofa Universitas Pamulang
Eko Sutono Universitas Pamulang

Keywords:

Quantitative Association Rule, Data Mining, Decision Optimization, Elementary School

Abstract

This study examines the application of the Quantitative Association Rule method to optimize the selection process of new students at SD IT Seluas Cita Nusantara. The main problem faced by the school is the difficulty in determining objective and relevant selection criteria; therefore, the aim of this research is to identify patterns of relationships among variables that support more accurate decision-making. The method employed is Association Rule Mining using parameters of support, confidence, and lift to analyze student candidate data. The results indicate that academic test scores are the dominant factor influencing selection success, with a confidence level of 0.82 and lift of 1.45. Non-academic factors such as participation in extracurricular activities (confidence 0.68; lift 1.20) and strong parental support (confidence 0.75; lift 1.30) also show significant contributions. The analysis reveals that combining academic and non-academic factors enhances the comprehensiveness of the selection process. The conclusion is that the Quantitative Association Rule method is effective in supporting schools to make more transparent, objective, and data-driven decisions. It is suggested that schools integrate these analytical findings into admission policies to improve accuracy and fairness in student selection.

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