Penerapan Algoritma K-Nearest Neighbor dengan Euclidean Distance untuk Menentukan Kelompok Uang Kuliah Tunggal Mahasiswa

Authors

  • Fenny Purwani Program Studi Sistem Informasi, UIN Raden Fatah Palembang
  • Ragil Tri Wahyudi Program Studi Sistem Informasi, UIN Raden Fatah Palembang
  • Irfan Dwi Jaya Program Studi Sistem Informasi, UIN Raden Fatah Palembang

DOI:

https://doi.org/10.29408/edumatic.v6i2.6547

Keywords:

data mining, classification, k-nearest neighbor, euclidean distance

Abstract

Single tuition fee or called UKT is the amount of tuition fee determined based on the student's economic ability. In its application, there are still many students who object to the UKT group that is obtained. Therefore, the university must apply the right and accurate method in determined the UKT group. This study aims to obtain the result of student’s UKT group classification using the K-Nearest Neighbor (KNN) algorithm with Euclidean Distance calculation and determine the accuracy of the algorithm with the optimal k value. This study used a quantitative method with a descriptive approach. The data collection techniques used are interviews, literature study, and documentation. The data that has been collected is 1,650 student’s UKT verification data for 2019-2021 which be processed with data mining using the RStudio software. The results showed that the classification with KNN can be applied in determined student’s UKT. With data testing many as 320 students, 23 students were determined to get UKT I, 149 UKT II, 129 UKT III, 32 UKT IV, and 2 students got UKT V. The accuracy of the algorithm is 87.58% in the Good Classification category. The optimal k for KNN obtained with K-Fold Cross Validation is k=1.

References

Banjarsari, M. A., Budiman, I., & Farmadi, A. (2015). Penerapan K-Optimal Pada Algoritma Knn untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer Fmipa Unlam Berdasarkan IP Sampai Dengan Semester 4. Klik, 2(2), 50-64.

Binabar, S. W., & Ivandari. (2017). Optimasi Parameter K pada Algoritma KNN untuk Deteksi Penyakit Kanker Payudara. IC-Tech, 13(1), 11–18.

Effendi, M., & Latifah, N. A. (2021). Penetapan Harga Jasa Pendidikan di Perguruan Tinggi Keagamaan Islam Negeri (PTKIN). EDUKASIA: Jurnal Pendidikan Dan Pembelajaran, 2(2), 127–143.

Hidayat, W., Utami, E., Iskandar, A. F., Hartanto, A. D., & Prasetio, A. B. (2021). Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19. Edumatic: Jurnal Pendidikan Informatika, 5(2), 167–176. https://doi.org/10.29408/edumatic.v5i2.3664

Laksono, E., Basuki, A., & Bachtiar, F. (2020). Optimization of k value in knn algorithm for spam and ham email classification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(2), 377-383. https://doi.org/10.29207/resti.v4i2.1845

Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons. https://doi.org/10.1002/9781118874059

Maulidah, M., Gata, W., Aulianita, R., & Agustyaningrum, C. I. (2020). Algoritma Klasifikasi Decision Tree Untuk Rekomendasi Buku Berdasarkan Kategori Buku. E-Bisnis: Jurnal Ilmiah Ekonomi dan Bisnis, 13(2), 89-96. https://doi.org/10.51903/e-bisnis.v13i2.251

Muktamar, B. A., Setiawan, N. A., & Adji, T. B. (2015). Analisis perbandingan tingkat akurasi algoritma naïve bayes classifier dengan correlated-naïve bayes classifier. Semnasteknomedia Online, 3(1), 2-1.

Nishom, M. (2019). Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square. Jurnal Informatika, 4(01), 20-24. https://doi.org/10.30591/jpit.v4i1.1253

Nuranti, M., Aini, M. N., & Enri, U. (2021). Komparasi Distance Measure Pada K-Medoids Clustering untuk Pengelompokkan Penyakit Ispa. Edumatic: Jurnal Pendidikan Informatika, 5(1), 99–107. https://doi.org/10.29408/edumatic.v5i1.3359

Nurmalasari, M. D., Kusrini, K., & Sudarmawan, S. (2021). Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes. Jurnal Komtika (Komputasi dan Informatika), 5(1), 52-59. https://doi.org/10.31603/komtika.v5i1.5140

Primajaya, A., Sari, B. N., & Khusaeri, A. (2020). Prediksi Potensi Kebakaran Hutan dengan Algoritma Klasifikasi C4. 5 Studi Kasus Provinsi Kalimantan Barat. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 6(2), 188-192. https://doi.org/10.26418/jp.v6i2.37834

Purnomo, A., & Saifullah, S. (2022). Tinjauan Utilitarianisme Hukum Atas Penerapan Regulasi Uang Kuliah Tunggal (UKT) di Perguruan Tinggi Keagamaan Islam Negeri. AL-MANHAJ: Jurnal Hukum Dan Pranata Sosial Islam, 4(2), 229–240.

Purwaningsih, E., & Nurelasari, E. (2021). Penerapan K-Nearest Neighbor Untuk Klasifikasi Tingkat Kelulusan Pada Siswa. Syntax: Jurnal Informatika, 10(01), 46-56. https://doi.org/10.35706/syji.v10i01.5173

Retnoningsih, Y. D., & Marom, A. (2017). Analisis Kebijakan Penyelenggaraan Pendidikan Berbasis Uang Kuliah Tunggal Bagi Perguruan Tinggi Negeri Fakultas Ilmu Sosial Dan Ilmu Politik Universitas Diponegoro Semarang Jawa Tengah. Journal of Public Policy and Management Review, 6(2), 482-497.

Schuh, G., Reinhart, G., Prote, J.-P., Sauermann, F., Horsthofer, J., Oppolzer, F., & Knoll, D. (2019). Data mining definitions and applications for the management of production complexity. Procedia CIRP, 81, 874–879. https://doi.org/10.1016/j.procir.2019.03.217

Setianto, Y. A., Kusrini, K., & Henderi, H. (2019). Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur. Creative Information Technology Journal, 5(3), 232-241. https://doi.org/10.24076/citec.2018v5i3.179

Simanjuntak, T. H., Mahmudy, W. F., & Sutrisno. (2017). Implementasi Modified K-Nearest Neighbor Dengan Otomatisasi Nilai K Pada Pengklasifikasian Penyakit Tanaman Kedelai. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 1 (2), 75-79.

Sukamto, S., Adriyani, Y., & Aulia, R. (2020). Prediksi Kelompok UKT Mahasiswa Menggunakan Algoritma K-Nearest Neighbor. JUITA: Jurnal Informatika, 8(1), 121-130. https://doi.org/10.30595/juita.v8i1.6267

Wahyudi, M. I., Wibowo, E. W., & Sopiullah, S. (2022). Web-Based Face Recognition using Line Edge Detection and Euclidean Distance Method. Edumatic: Jurnal Pendidikan Informatika, 6(1), 135–142. https://doi.org/10.29408/edumatic.v6i1.5525

Witten, I. H., Frank, E., Hall, M. a., & Mark, A. (2011). Data Mining: Practical Machine Learning Tools And Techniques. Amsterdam: Elsevier.

Yustanti, W. (2012). Algoritma K-Nearest Neighbour untuk Memprediksi Harga Jual Tanah. Jurnal Matematika, Statistika dan Komputasi, 9(1), 57-68.

Zong, C., Xia, R., & Zhang, J. (2021). Text Data Mining. Singapore: Springer. https://doi.org/10.1007/978-981-16-0100-2

Downloads

Published

2022-12-20