Implemetasi Machine Learning Metode K-Nearest Neighbor Untuk Memprediksi Status Kelulusan Mahasiswa Program Studi Informatika Universitas Dehasen Bengkulu
Abstract
Student graduation rates are a key performance indicator that’s really crucial for the quality and reputation of a university. However, study delays and the risk of dropping out are still serious challenges for the Informatics Study Program at Dehasen University Bengkulu. This research focuses on tackling these issues by developing an early prediction system for student graduation status to help support more proactive academic decision-making. This research uses an Educational Data Mining (EDM) approach with the Knowledge Discovery in Databases (KDD) framework to process student academic datasets. It implements the K-Nearest Neighbor (KNN) algorithm. Based on the testing results with the hyperparameter K=5, the KNN model achieved an accuracy rate of 50%, which indicates that the decision boundary between students who pass and those who don't cannot be significantly separated by the numeric academic variables currently used. This finding suggests that further development is needed by adding latent variables or sociological data, as well as more specific feature selection techniques to better capture student behavior patterns and improve the model's performance in the future.
Downloads
References
[2] Agustina, F. D., Arif, M., & Ahmad, S. (2025). Systematic Literature Review atas Kinerja Algoritma KNN, Naïve Bayes, dan Decision Tree pada Berbagai Studi Prediksi dan Klasifikasi. Jurnal Jawara Sistem Informasi, 3(1).
[3] Ahmad, N., Hafizh, S., & Sulthanah, R. (2024). Prediksi Kelulusan Mata Kuliah Mahasiswa Teknologi Informasi Menggunakan Algoritma K-Nearest Neighbor. Jurnal Manajemen Informatika (JAMIKA),14(2),135–149. https://doi.org /10.34010/jamika.v14i2.12454
[4] Alfarizi, M. R. sirfatullah, Al-farish, M. Z., Taufiqurrahman, M., Ardiansah, G., & Elgar, M. (2023). Penggunaan Python Sebagai Bahasa Pemrograman untuk Machine Learning dan Deep Learning. Karya Ilmiah Mahasiswa Bertauhid (KARIMAH TAUHID), 2(1), 1–6.
[5] Andriani, S., Nazir, A., Candra, R. M., Syafria, F., & Afrianty, I. (2023). Implementasi Algoritma K-Nearest Neighbor Untuk Menentukan Klasifikasi Kelulusan Mahasiswa Teknik Informatika. Journal of Computer System and Informatics (JoSYC), 4(4), 922–930. https://doi.org/10.47065/josyc.v4i4.3914
[6] Fahmi, M. N. (2023). Implementasi Mechine Learning menggunakan Python Library : Scikit-Learn (Supervised dan Unsupervised Learning). Sains Data Jurnal Studi Matematika Dan Teknologi, 1(2), 87–96. https://doi.org/10.52620 /sainsdata.v1i2.31
[7] Faisal, M., Utami, W. S., & Parmica, S. (2023). Implementasi Algoritma K-Nearest Neighbor (KNN) Dalam Memprediksi Indeks Kemiskinan. Journal Sensi, 9(1), 11–23. https://doi.org/10.33050/ sensi.v9i1.2616
[8] Febryana, S., Wulandari, I. A., & Prabowo, D. (2024). Rancang Bangun Aplikasi Pengolahan Data Inventory Barang Berbasis Desktop Pada Cv. Hamim Group. Jurnal Mahasiswa Sistem Informasi (JMSI), 6(1), 24–35. https://doi.org/10.24127/jmsi.v6i1.7553
[9] Hasanah, M. A., Soim, S., & Handayani, A. S. (2021). Implementasi CRISP-DM model menggunakan metode decision tree dengan algoritma CART untuk prediksi curah hujan berpotensi banjir. Journal of Applied Informatics and Computing (JAIC), 5(2), 103. http://jurnal.polibatam.ac.id/index.php/JAIC
[10] Iriane, R., & Nurfaizah. (2023). Penerapan Data Mining Untuk Prediksi Penjualan Produk Pangan Hewan Menggunakan Metode K-Nearest Neighbor. KLIK: Kajian Ilmiah Informatika Dan Komputer, 3(5), 509–515. https://djournals.com/klik
[11] Irsabmalfi, R., & Rahmadewi, R. (2025). Deteksi Anatomi Gigi Menggunakan Arsitektur YOLOV8 (You Only Look Once) Untuk Pengenalan Jenis-Jenis Gigi. JATI (Jurnal Mahasiswa Teknik Informatika), 9(3), 4870–4877.
[2] Moonallika, P. S. C., Fredlina, K. Q., & Sudiatmika, I. B. K. (2020). Penerapan Data Mining Untuk Memprediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes Classifier ( Studi Kasus STMIK Primakara ). Progresif: Jurnal Ilmiah Komputer, 16(1), 47–56. http://ojs.stmik-banjarbaru.ac.id/index. php/progresif/article/view/427
[13] Nahjan, M. R., Heryana, N., & Voutama, A. (2023). Implementasi Rapidminer Dengan Metode Clustering K-Means Untuk Analisa Penjualan Pada Toko Oj Cell. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 101–104.
[14] Naibaho, A. F. A., & Zahra, A. (2023). Prediksi Kelulusan Siswa Sekolah Menengah Pertama Menggunakan Machine Learning. Jurnal Informatika Dan Teknik Elektro Terapan, 11(3). https://doi.org/10.23960/jitet.v11i3.3056
[15] Naldy, E. T., & Andri, A. (2021). Penerapan Data Mining Untuk Analisis Daftar Pembelian Konsumen Dengan Menggunakan Algoritma Apriori Pada Transaksi Penjualan Toko Bangunan MDN. Jurnal Nasional Ilmu Komputer, 2(2),89–101. https://doi.org/10.47747 /jurnalnik.v2i2.525
[16] Noija, H. C., Wemaf, P. A., Nurdianty, O. A., Sohilait, W., Haumahu, S., Yusuf, H., Tomagola, K., Wa Rindi La Ana, & Salaiswa, T. S. (2023). Perancangan Sistem Informasi Akuntansi Untuk Analisis Siklus Pendapatan Pada Orantata Celullar Menggunakan DFD Dan Flowchart. Jurnal Bisnis Dan Manajemen (JURBISMAN), 1(2), 577–592. https://doi.org/10.61930/jurbisman.v1i2.188
[17] Octarina, S., Puspita, F. M., Yuliza, E., & Indrawati. (2025). Pendampingan Penggunaan Google Colab Pada Pembelajaran Python Dan Machine Learning Bagi Dosen Matematika DI PALEMBANG. Jurnal Terpadu, 6(1), 56–66.
[18] Rahmat, A., Hardi, H., Syam, F. A., Zamzami, Z., Febriadi, B., & Windarto, A. P. (2021). Utilization of the field of data mining in mapping the area of the Human Development Index (HDI) in Indonesia. Journal of Physics: Conference Series, 1783(1). https://doi.org/10.1088/1742-6596/1783/1/012035
[19] Santoso, P., Abijono, H., & Anggreini, N. L. (2021). Algoritma Supervised Learning Dan Unsupervised Learning Dalam Pengolahan Data. Jurnal Teknologi Terapan: G-Tech, 4(2), 315–318. https://doi.org/10.33379/gtech.v4i2.635
[20] Sartika, D., & Jumadi, J. (2023). Implementasi Data Mining Untuk Pengelompokkan Wilayah Pelanggaran Lalu Lintas Menggunakan Metode K-Means Pada Polres Bengkulu. Journal of Science and Social Research, 4307(2), 345–349. http://jurnal.goretanpena.com/ index.php/JSSR
[21] Surbakti, N. M., Angelyca, Talia, A., Perangin-Angin, C. B., Nainggolan, D. O., Friskauly, N. D., & Tumorang, S. R. B. (2024). Penggunaan Bahasa Pemrograman Python dalam Pembelajaran Kalkulus Fungsi Dua Variabel. Algoritma : Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian Dan Angkasa, 2(3), 98–107.
[22] Syam, S., Tokoro, Y., Judijanto, L., Garonga, M., Sinaga, F. M., Umar, N., Handika, I. P. S., Iin, J. N., Apriyanto, A., & Sitanggang, A. T. (2024). Data Mining: Teori dan Penerapannya dalam Berbagai Bidang. PT. Sonpedia Publishing Indonesia.
[23] Tumbilung, C. F., & Efendi, R. (2025). Prediksi Risiko Drop Out Mahasiswa Menggunakan Model Machine Learning Berbasis Data Akademik (Studi Kasus : Universitas XYZ). JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 16(2), 176–183. https://ejurnal.provisi.ac.id/index.php/JTIKP/article/view/1257/878
[24] Wardhana, R. G., Wang, G., & Sibuea, F. (2023). PENERAPAN MACHINE LEARNING DALAM PREDIKSI TINGKAT KASUS PENYAKIT DI INDONESIA. Journal of Information System Management (JOISM), 5(1).
[25] Widodo, A. P., & Setiawan, A. (2025). Systematic Literature Review : Peran Machine Learning dalam Manajemen Sumber Daya Manusia. Optimal: Jurnal Ekonomi Dan Manajemen, 5, 650–665. https://doi.org/https://doi.org/10.55606/optimal.v5i4.8089
[26] Wiguna, D. P., Juliana, L., & Ginting, S. E. (2025). Aplikasi Google Colab Berbasis Python dalam Menerapkan Teori Pohon dengan Algoritma Random Forest Classifier. Scripta Technica : Journal of Engineering and Applied Technology, 1(2), 261–270.
[27] Wulandari, G., Sasmita, S., & Rahmadayanti, F. (2024). Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Obat. J. Tek. Inf. Dan Komput, 7(1), 144–150.
[28] Yoliad, D. N. (2023). Data mining Dalam Analisis Tingkat Penjualan Barang ElektronikMenggunakan Algoritma K-means. Insearch (Information Syst. Res. J., Vol. 3, No. 1.
Copyright (c) 2026 AL Kausar AH, Devi Sartika; Ockhy Jey Fhiter
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
An author who publishes in Jurnal Media Infotama agrees to the following terms:The author holds the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-Share Alike 4.0 License which allows others to share the work with acknowledgment of the work's authorship and initial publication in this journal.Submission of a manuscript implies that the submitted work has not been previously published (except as part of a thesis or report, or abstract); that it is not being considered for publication elsewhere; that its publication has been approved by all co-authors. If and when a manuscript is accepted for publication, the author retains the copyright and retains the publishing rights without limitation.
For new inventions, authors are advised to administer the patent before publication. The license type is CC-BY-SA 4.0.
MEDIA INFORMATION REVIEW: Journal of the Faculty of Computer Science is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.You are free to:Share
— copy and redistribute material in any medium or formatAdapt
— remix, modify and develop materialfor any purpose, even commercial.
The licensor cannot revoke this freedom as long as you follow the license terms
.png)
