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All Journal AL KAUNIYAH Jurnal Ilmiah Mahasiswa FEB TIN: TERAPAN INFORMATIKA NUSANTARA JRAK (Jurnal Riset Akuntansi dan Bisnis) (e-Journal) Pena : Jurnal Ilmu Pengetahuan dan Teknologi Jurnal Ilmu Manajemen Retail Universitas Muhammadiyah Sukabumi (JIMAT UMMI) Sawala : Jurnal pengabdian Masyarakat Pembangunan Sosial, Desa dan Masyarakat El-Ecosy : Jurnal Ekonomi dan Keuangan Islam e_Jurnal Ilmiah Riset Akuntansi Kohesi: Jurnal Sains dan Teknologi Cantaka: Jurnal Ilmu Ekonomi dan Manajemen JOUSIP Jurnal Ilmiah Akuntansi International Journal of Applied Mathematics and Computing. Journal of Citizen Research and Development Critical Review of English-Arabic World Journal
Nurul Khoiriyah
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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
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i4.111

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.
Co-Authors Aini Lubis, Nazwa Septi Amanda, Ayuni Sekar Amelia, Dinda Nur Andy Kurniawan Arinal, Veri Aristya A, Nadzarudin Arso Setyaji Atamimi, Abdul Basit Badrus Zaman Betty Yel, Mesra Br Sitorus, Dini Sastra Budi Sukardi Cornelia Susilo, Maurinda Devita, Tsara Dita Safira Faiza Hawa Farah Wulandari Pangestuty Fiktor Kurnia Fitriani, Icha Hilman Syaeful Alam Junaidi Junaidi Kristi, Anastasia Paskalia Luthfiyyah, Mahirah Azzah M. Cholid Mawardi Mandhegani, Gilang Putra Manurung, Bela Wahyuni Muaz, Abdul Mufidah, Zahratul Maujudatul Nazwa, Khairin Nurhayati Nurhayati Nurul Azkia, Aisha Prakoso Angga I Rahmi, Delila Amelia Rina Hidayati Pratiwi S. Rosalinda, S. Sofia, Azyka Sri Layla Wahyu I Tanjung, Emilda Tri Wahyudi Usiono Usiono Zahra Harahap, Atika Zali, Muhammad