KLASIFIKASI ALGORITMA K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA S1
COMPARATION OF K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE TO PREDICT UNDERGRADUATE STUDENTS TO GRADUATE ON TIME
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Abstract
Students are a crucial factor that must be considered in seriously evaluating study programs. The indicator of the success of the study program is the length of time it takes to complete the study. The study period is the time when students complete their studies. In addition, student study time reflects the level of student learning performance. In a broader perspective, the average student study time affects the quality of study programs and therefore student study time is used as one of the criteria in determining the assessment by the National Accreditation Board for Higher Education (BAN PT). The purpose of this study was to understand how well the K-Nearest Neighbor, Naive Bayes, Decision Tree performed to predict undergraduate students of the Law Study Program, Faculty of Law, Sebelas Maret University, graduating on time using the RapidMiner application. From the results of the testing and prediction process with the RapidMiner application using the three methods that have been carried out. The K-Nerest Neighbor (KNN) method obtained an accuracy of 96.67%, in the prediction test using the Naïve Bayes method it obtained an accuracy of 77.33%, while the Decision Tree method obtained an accuracy of 94.00%. So that the K-NN method is the best method in comparative classification in predicting student graduation on time with a predicted accuracy value of 96.67%.
References
E. Etriyanti, D. Syamsuar, and Y. N. Kunang, "Implementasi Data Mining Menggunakan Algoritme Naive Bayes Classifier Dan C4.5 Untuk Memprediksi Kelulusan Mahasiswa," Telematika, vol. 13, no. 1, pp. 56-57, 2020.
Azahari, Yulindawati, D. Rosita, and S. Mallala, "Komparasi Data Mining Naive Bayes dan Neural Network Memprediksi Studi Mahasiswa S1," Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 7, no. 3 , pp. 443-452, 2018.
E. F. Wati, and B. Rudianto, "Penerapan Algoritma KNN, Naive Bayes dan C4.5 Dalam Memprediksi Kelulusan Mahasiswa," Jurnal Format, vol. 11, no. 2 , pp. 168-175, 2022.
I. A. Nikmatun, and I. Waspada, "Implementasi Data Mining Untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor," Jurnal Simetris, vol. 10, no. 2, pp. 421-432, 2019.
A. Budiyantara, Irwansyah, E. Prengki, P. A. Pratama, and N. Wiliani, "Komparasi Algortima Decision Tree, Naive Bayes dan K-Nearest Neighbor Untuk Memprediksi Mahasiswa Lulus Tepat Waktu," Jurnal Ilmu Pengetahuan dan Teknologi Komputer, vol. 5, no. 2 , pp. 265-270, 2020.
R. P. S. Putri, and I. Waspada, "Penerapan Algoritma C4.5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika," Jurnal Ilmu Komputer dan Informatika Khazanah Informatika, vol. 4, no. 1, pp. 1-7, 2018.
J. A. Samudra, S. Anraeni, and Herman, "Penerapan Metode K-Nearest Neighbor untuk Memprediksi Tingkat Kelulusan Mahasiswa Berbasis Web pada Fakultas Ilmu Komputer UMI," Busiti Buletin Sistem Informasi dan Teknologi Islam, vol. 1, no. 4, pp. 230-237, 2020.
L. Setiayani, M. Wahidin, D. Awaludin, and S. Purwani, "Analisis Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Data Mining Naive Bayes : Systematic Review," Faktor Exacta, vol. 13, no. 1, pp. 35-43, 2020.
Irmayansyah, and M. T. Kastrilia, "Penerapan Algoritma C4.5 untuk Prediksi Mahasiswa Berpotensi Lulus Tidak Tepat Waktu," Jurnal Ilmiah Teknologi - Informasi dan Sains, vol. 10, no. 2, pp. 9-18, 2020.
D. Safitri, S.S. Hilabi, and F. Nurapriani, "Analisis Penggunaan Algoritma Klasifikasi Dalam Prediksi Kelulusan Menggunakan Orange Data Mining," Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 8, no. 1, pp. 75-81, 2023.
N. Khasanah, A. Salim, N. Afni, R. Komarudin, and Y. I. Maulana, " Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes," Technologia Jurnal Ilmiah, vol. 13, no. 3, pp. 207-214, 2022.
P. S. C. Moonallika, K. Q. Fredlina, and I. B. K. Sudiatmika , "Penerapan Data Mining Untuk Memprediksi Kelulusan Mahasiswa Menggunakan ALlgorima Naive Bayes Classifier (Studi Kasus STMIK Primakara)," Progresif : Jurnal Ilmiah Komputer, vol. 16, no. 1, pp. 47-56, 2020.
A. Wibowo, and A. Rohman, "Prediksi Predikat Kelulusan Mahasiswa Menggunakan Naive Bayes dan Decision Tree pada Universitas XYZ," Expert Jurnal Manajemen Sistem Informasi dan Tteknologi, vol. 12, no. 2, pp. 104-112, 2022.
N. purwati, and A. D. Januanti, "Prediksi Tingkat Kelulusan Mahasiswa dengan Algoritma Naive Bayes," Jurnal Pepaduan Jurnal Ilmiah, Ilmu Komputer dan Sistem Informasi, vol. 2, no. 1, pp. 126-137, 2021.
I. Iskandar, L. Hiryanto, and J. Hendryli, "Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Decision Tree C4.5 Dengan Teknik Pruning," JIKSI Jurnal Ilmu Komputer dan Sistem Informasi, vol. 6, no. 1 , pp. 64-68, 2018.
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