Klasifikasi Penerimaan Peserta Didik Baru Berdasarkan Sistem Zonasi Menggunakan Algoritma K-Nearest Neighbors
DOI:
https://doi.org/10.47065/jieee.v5i1.2595Keywords:
Classification; K-Nearest Neighbors; Zoning System; Student AdmissionAbstract
The implementation of the zoning system in student admission (PPDB) often raises challenges in determining eligibility based on domicile, requiring a data-driven approach to support the selection process. This study aims to classify new students of SMPN 16 Bogor for the 2025 academic year using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 1,153 student records with attributes including longitude, latitude, distance from home to school, and zoning labels. Preprocessing involved data cleaning, label encoding, and feature standardization before splitting the data into 75% training and 25% testing sets. The optimal parameter was found at K=18 with a minimum error rate of 0.1591. Experimental results showed an accuracy of 97% for training data and 84% for testing data, indicating that the model performs reasonably well despite signs of overfitting. This research contributes by demonstrating that spatial attributes can be effectively integrated into zoning-based classification and provides a foundation for developing more objective and adaptive decision support systems in the context of student admissions.
Downloads
References
I. G. P. Darya, “Evaluasi implementasi sistem penerimaan peserta didik baru (PPDB) di Kota Balikpapan, Indonesia,” J. Penelit. Pendidik., vol. 20, no. 1, pp. 32–41, 2020.
A. Muhaimin, “Klasifikasi prestasi akademik siswa berdasarkan nilai rapor dan kedisiplinan.” Universitas Islam Negeri Maulana Malik Ibrahim, 2024.
V. Chugani, “Understanding Euclidean Distance: From Theory to Practice,” Datacamp. [Online]. Available: https://www.datacamp.com/tutorial/euclidean-distance
M. D. Nawar, F. Helmiah, and C. Latiffani, “Penerapan Metode Weighted Product (Wp) Penilaian Soft Skill Guru Dalam Belajar Di Mts. Hidayatul Ulumiyah Ujung Kubu,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 2, pp. 1022–1030, 2025.
R. K. Imani, S. H. Wijoyo, and F. Amalia, “Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Kemampuan Lulusan Siswa Dalam Bersaing untuk Mendapatkan Pekerjaan (Studi Kasus: SMK ‘SORE’ Tulungagung),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 10, 2024.
and S. S. J. I. Kartika, E. Santoso, “Penentuan Siswa Berprestasi Menggunakan KNN dan Weighted Product," J. Pengembang. Tekno. Inf., vol. 1., p. no. 5, pp. 352–360, 2017.
S. Widaningsih, “Penerapan Data Mining untuk Memprediksi Siswa Berprestasi dengan Menggunakan Algoritma K Nearest Neighbor,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 2598–2611, 2022.
J. A. Samudra, S. Anraeni, and H. Herman, “Penerapan Metode K-Nearest Neighbor Untuk Memprediksi Tingkat Kelulusan Mahasiswa Berbasis Web Pada Fakultas Ilmu Komputer UMI,” Bul. Sist. Inf. dan Teknol. Islam, vol. 1, no. 4, pp. 230–237, 2020.
N. Wati, “Prediksi Kelulusan Mahasiswa Menggunakan K-Nearest Neighbor Berbasis Particle Swarm optimization,” J. Teknol. Inf. Indones., vol. 6, no. 2, pp. 118–127, 2021.
A. Putri et al., “Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir: Comparison of K-NN, Naive Bayes and SVM Algorithms for Final-Year Student Graduation Prediction,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 20–26, 2023.
T. Nasution, “Implementasi algoritma K-Nearest Neighbor untuk penentuan kelulusan mahasiswa tepat waktu,” J. Perangkat Lunak, vol. 2, no. 1, pp. 1–14, 2020.
H. Henderi, T. Wahyuningsih, and E. Rahwanto, “Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer,” Int. J. Informatics Inf. Syst., vol. 4, no. 1, pp. 13–20, 2021.
J. Opitz, “A closer look at classification evaluation metrics and a critical reflection of common evaluation practice,” Trans. Assoc. Comput. Linguist., vol. 12, pp. 820–836, 2024.
?A. Amelia, M. Asfi, and R. Fahrudin, “Implementation of K-Nearest Neighbor Method for Selection of New Employee Candidates (Case Study: CV. Syntax Corporation Indonesia),” Eduvest - Journal of Universal Studies, vol. 4, no. 7, pp. 5742–5754, 2024, doi: 10.59188/eduvest.v4i7.1305.
?S. R. Cholil, T. Handayani, R. Prathivi, and T. Ardianita, “Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa,” IJCIT (Indonesian Journal on Computer and Information Technology), vol. 6, no. 2, pp. 118–127, 2021.
?K. Harian, “Pengertian Dataset dan Jenis-jenisnya,” Kumparan, 2021. [Online]. Available: https://kumparan.com/kabar-harian/pengertian-dataset-dan-jenis-jenisnya-1wtM6xNlkpQ
?V. A. D. Hidayatullah, A. Nilogiri, and H. A. Al Faruq, “Klasifikasi Siswa Berprestasi Menggunakan Metode K-Nearest Neighbor (KNN) Pada SMA Negeri 2 Situbondo,” Jurnal Smart Teknologi, vol. 1, no. 1, 2021. [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JST
?D. Intern, “Python: Pengertian, Contoh Penggunaan, dan Manfaat Mempelajarinya,” Dicoding Blog, 2023. [Online]. Available: https://www.dicoding.com/blog/python-pengertian-contoh-penggunaan-dan-manfaat-mempelajarinya/
?N. A. Makarim, “Peraturan Menteri Pendidikan, Kebudayaan, Riset, dan Teknologi Republik Indonesia Nomor 17 Tahun 2024 tentang Sistem Zonasi Cagar Budaya,” pp. 4–6, Feb. 2024.
?M. Miftah and S. Syamsurijal, “Strategi Pemanfaatan Lingkungan Pendidikan untuk Meningkatkan Motivasi Belajar Siswa,” Edu Cendikia: Jurnal Ilmiah Kependidikan, vol. 3, no. 01, pp. 72–83, 2023, doi: 10.47709/educendikia.v3i01.2251.
?S. B. Munthe, “Implementasi Algoritma K-Nearest Neighbor Tawar Menggunakan Ekstraksi Fitur ORB,” Skripsi, Universitas Medan Area, 2023.
?A. Oliver, “Mengenal Google Colab: Mulai dari Definisi, Cara Menggunakan, hingga Manfaatnya,” Glints, 2025. [Online]. Available: https://glints.com/id/lowongan/google-colab-adalah/
?D. Olivia, “Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Ketepatan Waktu Lulus Mahasiswa,” 2024.
?Kumparan, “Pengertian dan Istilah. Apa Itu Zonasi Sekolah? Ini Pengertian, Kelebihan, dan Kekurangannya,” Kumparan, 2024. [Online]. Available: https://kumparan.com/pengertian-dan-istilah/apa-itu-zonasi-sekolah-ini-pengertian-kelebihan-dan-kekurangannya-23yziXZJUAH
?N. Pradita, “Penerapan Data Mining sebagai Cara untuk Memprediksi Prestasi Siswa Berdasarkan Status Ekonomi dan Kedisiplinan Menggunakan Metode Regresi Linier Berganda,” Jurnal Ilmiah Mahasiswa Manajemen, Bisnis dan Akuntansi (JIMMBA), vol. 4, no. 5, pp. 683–696, 2022, doi: 10.32639/jimmba.v4i5.181.
?M. F. Shodiq and D. Darmawan, “Pengaruh Lingkungan Sekolah terhadap Hasil Belajar Siswa Sekolah Menengah Pertama,” IHSANIKA: Jurnal Pendidikan Agama Islam, vol. 3, no. 1, pp. 292–307, 2025.
?R. S. Wahono, “Data Mining,” in Mining of Massive Datasets, vol. 2, Jan. 2013. [Online]. Available: https://www.cambridge.org/core/product/identifier/CBO9781139058452A007/type/book_part
?Y. Yahya and W. Hidayanti, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Efektivitas Penjualan Vape (Rokok Elektrik) pada ‘Lombok Vape On,’” Infotek: Jurnal Informatika dan Teknologi, vol. 3, pp. 104–114, 2020, doi: 10.29408/jit.v3i2.2279.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Penerimaan Peserta Didik Baru Berdasarkan Sistem Zonasi Menggunakan Algoritma K-Nearest Neighbors
ARTICLE HISTORY
Issue
Section
Copyright (c) 2025 Muhammad Syah Fiqri, Andi Taufik
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
