Implementasi K-Nearest Neighbor Dalam Memprediksi Keterlambatan Pembayaran Biaya Kuliah Di Perguruan Tinggi - Muhammad Rhosyid Akhmad; Taghfirul Azhima Yoga Siswa (Progresif: Jurnal Ilmiah Komputer, 2022)
The delay in payment of tuition fees at the Muhammadiyah University of East Kalimantan for the 2020/2021 academic year reached 3,018 out of a total of 5,533 students. The number of latecomers is quite large because it exceeds half of the total students. It is deemed necessary to conduct an analysis related to the late payment, so that prevention and treatment can be carried out as early as possible. The purpose of this study is to determine the parameters of delay in paying tuition fees, implementing the K-Nearest Neighbor algorithm, and evaluating the performance of the algorithm using a confusion matrix. The amount of data used for the algorithm performance testing process is 12,408 records with a ratio of 80% training data and 20% testing data. The results of the evaluation test showed that the accuracy of k=3 was 52.82%, k=5 was 52.49%, k=7 was 52.37%, k=9 was 52.33%, and k=11 was 52.53%.  The best data test results were obtained at k = 3, namely 52.82%.Keywords: Accuracy; K-Nearest Neighbor; Tuition Fee Payment, Confusion matrix Abstrak. Keterlambatan pembayaran biaya kuliah di Universitas Muhammadiyah Kalimantan Timur tahun ajaran 2020/2021 mencapai 3.018 dari total keseluruhan 5.533 mahasiswa. Jumlah yang terlambat tersebut tergolong cukup banyak karena melebihi separuh dari keseluruhan mahasiswa. Dipandang perlu untuk melakukan analisis berkaitan dengan keterlambatan pembayaran tersebut, agar dapat dilakukan pencegahan dan penanganan sedini mungkin. Tujuan penelitian ini adalah menentukan parameter keterlambatan dari pembayaran biaya kuliah, mengimplementasi algoritma K-Nearest Neighbor, dan mengevaluasi kinerja algortima menggunakan Confusion Matrix. Jumlah data yang digunakan untuk proses pengujian kinerja algoritma adalah 12.408 record dengan rasio 80% data training dan 20% data testing. Hasil pengujian evaluasi didapatkan akurasi k=3 sebesar 52,82%, k=5 sebesar 52,49%, k=7 sebesar 52,37%, k=9 sebesar 52,33%, dan k=11 sebesar 52,53%. Hasil pengujian data terbaik didapatkan pada nilai k=3 yaitu 52,82%.Kata kunci: Akurasi; K-Nearest Neighbor; Pembayaran biaya kuliah, Confusion matrix