Data Mining Classification in The New Student Admission Process Using The K-Nearest Neighbors Method : Case Study: Yapmi Boarding School

Veri Arinal; Tri Wahyudi; Mesra Betty Yel; Nurul Khoiriyah

doi:10.62951/ijamc.v1i4.111

Data Mining Classification in The New Student Admission Process Using The K-Nearest Neighbors Method

Case Study: Yapmi Boarding School

Authors

Veri Arinal Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOMCKI) Jakarta
Tri Wahyudi Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOMCKI) Jakarta
Mesra Betty Yel Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOMCKI) Jakarta
Nurul Khoiriyah Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika (STIKOMCKI) Jakarta

DOI:

https://doi.org/10.62951/ijamc.v1i4.111

Keywords:

Classification, Data Mining, K-Nearest Neighbors, Student Admission, Student Selection

Abstract

The new student admission process is an important activity in educational institutions to ensure that prospective students meet the established admission criteria. However, the selection process is often conducted manually, making it less efficient and prone to subjective assessments. This study aims to implement a data mining classification approach using the K-Nearest Neighbors (K-NN) method to support decision-making in the new student admission process at Yapmi Boarding School. The research utilizes historical admission data consisting of academic scores, interview results, and other admission criteria as classification attributes. The K-NN algorithm was applied to classify prospective students into accepted and rejected categories based on the similarity of their characteristics to previously evaluated applicants. The research methodology includes data collection, preprocessing, classification modeling, and performance evaluation using accuracy metrics. The results demonstrate that the K-NN method is capable of classifying prospective students effectively and can assist admission committees in making more objective and accurate decisions. The implementation of this model contributes to improving the efficiency, consistency, and reliability of the student admission process at Yapmi Boarding School. Therefore, the K-NN algorithm can be considered a viable alternative for supporting educational admission decision systems.

References

Abualhaj, M. M., Abu-Shareha, A. A., Shambour, Q., Alsaaidah, B., Al-Khatib, R., & Anbar, M. (2024). Applications of K-Nearest Neighbor in classification and prediction: A review of recent developments. Journal of Computer Science and Information Systems, *20*(2), 145–160.

Adenugraha, S. P., Arinal, V., & Mulyana, D. I. (2022). Klasifikasi kematangan buah pisang ambon menggunakan metode KNN dan PCA berdasarkan citra RGB dan HSV. Jurnal Media Informatika Budidarma, *6*(1), 9.

Arfah, D. J., Masrizal, M., & Irmayanti. (2024). Machine learning to predict student satisfaction level using KNN method and Naive Bayes method. Sinkron: Jurnal dan Penelitian Teknik Informatika, *8*(3), 1895–1908. https://doi.org/10.33395/sinkron.v8i3.13914

Arinal, V., & Rusmarhadi, I. (2024). Implementasi data mining untuk menentukan strategi penjualan produk UMKM Raja Geprek pada pola pembelian konsumen menggunakan algoritma Apriori. INTECOMS: Jurnal Informasi Teknologi dan Ilmu Komputer, *7*(5), 1482–1494.

Arinal, V., & Safira, D. (2025). Sistem data mining pengelolaan transaksi penjualan pulsa pada Sujase Cell Jakarta Barat. Jurnal Ilmiah Sosial dan Sains, *8*(2), 3186–3192.

Deteksi dini siswa korban bullying dalam penerapan data mining menggunakan algoritma C4.5 pada SMP IDN Jonggol. (2025). Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), *9*(1). https://doi.org/10.35870/jtik.v9i1.3022

Kumar, A., DiJohnson, T., Edwards, R., & Walker, L. (2021). The application of adaptive minimum match k-nearest neighbors to identify at-risk students in health professions education. Computers in Human Behavior Reports, *4*, 100128.

Kurniawan, A., & Saputra, B. (2022). Implementation of KNN for student selection. Journal of Education, 5(2), 15–22.

Maquen-Niño, G. L. E., Miguel-Flores, M. A., Aurich-Mio, L. Y., Adrianzén-Olano, I., de la Cruz Vélez de Villa, P. E., & Castro-Cárdenas, D. M. (2025). Machine learning model for classifying high school students’ academic performance in mathematics amidst the COVID-19 context. Journal of Technology and Science Education, *15*(2), 322–334. https://doi.org/10.3926/jotse.2945

Prahmana, I. G., et al. (2025). Application of the K-Nearest Neighbor (KNN) algorithm in classification systems. Journal of Artificial Intelligence and Engineering Applications, *4*(1), 55–67.

Setiawan, R., & Arinal, V. (2025). Development of a web-based educational information system using the RAD method: A case study at Persahabatan Hospital. Lembaga Komunitas Informasi Teknologi Aceh, *5*.

Sudipa, I. G. I., Azdy, R. A., Arfiani, I., Setiohardjo, N. M., & Sumiyatun. (2024). Leveraging K-Nearest Neighbors for enhanced fruit classification and quality assessment. International Journal of Data Science, *5*(1), 1–12. https://doi.org/10.56705/ijodas.v5i1.125

Yaqien, A., & Arinal, V. (2026). Implementasi sistem informasi pengukuran kinerja karyawan di PT Hetra Vexa Group dengan pendekatan Key Performance Indikator. Jurnal Indonesia Manajemen Informatika dan Komunikasi, *7*(1), 32–46.

Yasin, E. (2025). Machine learning-based classification of student adaptability in online education using feature engineering techniques. TIERS Information Technology Journal, 131–142.

Yel, M. B., & others. (2024). Pelatihan penggunaan tools WEKA untuk kepentingan proses data mining di ITS NU Pekalongan. ABDINE: Jurnal Pengabdian Masyarakat.

Downloads

Published

2026-06-02

How to Cite

Veri Arinal, Tri Wahyudi, Mesra Betty Yel, & Nurul Khoiriyah. (2026). Data Mining Classification in The New Student Admission Process Using The K-Nearest Neighbors Method : Case Study: Yapmi Boarding School. International Journal of Applied Mathematics and Computing, 1(4), 62–69. https://doi.org/10.62951/ijamc.v1i4.111