Keywords
Abstract
School is a formal education unit where teachers teach various disciplines which aim to make students become superior human beings. Education is important for humans. In general, people in Indonesia want their sons and daughters to be able to get an education at State Universities (PTN). There are many paths that can be taken to PTN, one of which is the National Selection for State University Entrance (SNMPTN) which is the PTN entrance route without a test or selection of report cards. However, the quota for acceptance of PTN through the SNMPTN pathway is at least 20% and the passing grade of each PTN cannot be determined so that the possibility of being accepted into PTN through the SNMPTN route is unknown. Therefore, to be able to predict students who can be accepted into PTN through the SNMPTN path, predictions of SNMPTN acceptance are made using classification data mining with the C4.5 and Naive Bayes algorithm. The goal is that with students knowing the possibility of being accepted into PTN through the SNMPTN pathway, it is hoped that more students can be accepted by PTN through the SNMPTN pathway. This study shows that the prediction of PTN acceptance through the SNMPTN path using data mining classification with the C4.5 algorithm produces a much higher accuracy of 85,09% and the AUC value 0,873, while the Naive Bayes algorithm produces an accuracy of 63,01% and the AUC value
References
E. Darmawani, “JUANG: Jurnal Wahana Konseling HIV,” Metod. Ekspositori Dalam Pelaks. Bimbing. dan Konsleing Klasikal, vol. 1, no. 2, pp. 30–44, 2018.
Lembaga, M. Perguruan, T. Ltmpt, and N. Masuk, “Jumlah Pendaftar SNMPTN 2022 Meningkat , Ini Rinciannya,” p. 2022, 2022.
H. Hasanah, N. A. Sudibyo, and E. Kurniawan, “Prediksi Jurusan pada Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) Menggunakan Metode Klasifikasi Naïve Bayes,” DoubleClick J. Comput. Inf. Technol., vol. 4, no. 1, p. 55, 2020, doi: 10.25273/doubleclick.v4i1.6623.
L. K. Simanjuntak, T. Y. M. Sihite, M. Mesran, N. Kurniasih, and Y. Yuhandri, “Sistem Pendukung Keputusan SNMPTN Jalur Undangan Dengan Metode Electre,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 3, no. 3, p. 14, 2018, doi: 10.30645/jurasik.v3i0.63.
J. K. Nazarius, “Analisis Pemilihan Siswa Untuk Jalur SNMPTN dengan Metode Weighted Product ( WP ) Dan Weighted Sum Model ( WSM ),” vol. 5, pp. 135–142, 2021.
W. R. Izzati, M. Komarudin, H. D. Septama, and Y. Mulyani, “Analisis Potensi Asal Sekolah pada Jalur Penerimaan Mahasiswa Baru di Universitas Lampung menggunakan Algoritma K-Means,” Electrician, vol. 13, no. 1, p. 7, 2019, doi: 10.23960/elc.v13n1.2087.
C.-T. Hsieh and C.-S. Hu, “Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with SVM,” J. Appl. Res. Technol., vol. 12, no. 6, pp. 1014–1024, 2014, doi: 10.1016/S1665-6423(14)71662-1.
A. Purwanto, A. Primajaya, and A. Voutama, “Penerapan Algoritma C4.5 Dalam Prediksi Potensi Tingkat Kasus Pneumonia Di Kabupaten Karawang,” J. Sist. dan Teknol. Inf., vol. 8, no. 4, p. 390, 2020, doi: 10.26418/justin.v8i4.41959.
E. F. Wati and B. Rudianto, “Penerapan Algoritma KNN, Naive Bayes Dan C4.5 Dalam Memprediksi Kelulusan Mahasiswa,” Format J. Ilm. Tek. Inform., vol. 11, no. 2, p. 168, 2023, doi: 10.22441/format.2022.v11.i2.009.
R. P. S. Putri and I. Waspada, “Penerapan Algoritma C4.5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 1, pp. 1–7, 2018, doi: 10.23917/khif.v4i1.5975.
S. Lestari and A. Suryadi, “Model Klasifikasi Kinerja Dan Seleksidosen Berprestasi Dengan,” Proseding Semin. Bisnis Teknol., pp. 15–16, 2014.
N. Khasanah and A. Salim, “Rachman Komarudin 4) , Yana Iqbal Maulana 5) 1) Teknik Informatika, Fakultas Teknologi Informasi, Universitas Nusa Mandiri 2,3) Sistem Informasi, Fakultas Teknologi Informasi, Universitas Bina Sarana Informatika 4) Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusa Mandiri 5) Teknik Informatika,” Fak. Teknol. Inf., vol. 13, no. 3, pp. 207–214, 2022.
N. et al Maulina, “PERBANDINGAN TINGKAT ANSIETAS MAHASISWA KEDOKTERAN YANG DITERIMA MELALUI JALUR SNMPTN, SBMPTN DAN MANDIRI DAN KECENDERUNGAN CABIN FEVER DALAM MELAKSANAKAN UJIAN BLOK PADA MASA PANDEMI,” Syntax Lit. J. Ilm. Indones., vol. 6, no. 2, p. 6, 2021.
A. Krisna Ferdinan Leo Simanjuntak, Annita Carolina Br Barus, “Implementasi Metode Decision Tree Dan Algoritma C4.5 Untuk Klasifikasi Kepribadian Masyarakat,” JOISIE J. Inf. Syst. Informatics Eng., vol. 5, no. 1, pp. 51–59, 2021.
S. Syahputra, S. Ramadani, A. Manaor, and H. Pardede, “MENENTUKAN STRATEGI PROMOSI MENGGUNAKAN ALGORITMA CLUSTERING K-MEANS didapatkan informasi anggota cluster 1 terdiri dari 164 siswa yang berasal dari kecamatan Kuala sebanyak 75 siswa , dengan asal sekolah terbanyak dari SMP Negeri 1 Salapian sebanyak 21 s,” vol. 4, no. 1, pp. 7–14, 2020.
D. T. Larose, Discovering Knowledge in Data an introduction to data mining. 2005.
F. Gorunescu, Data Mining Concepts, Models and Techniques. Berlin: Springer, 2011.
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques. 2016.
D. Haryono, Y. Zulianda, Wirta, and Lusiana, “Sistem Pendeteksian Serangan Jaringan Local Area Network ( Lan ) Menggunakan Algoritma Naive Bayes,” JOISIE (Journal …, vol. 5, no. 1, pp. 1–8, 2021.
K. Umam, D. Puspitasari, and A. Nurhadi, “Penerapan Algoritma C4.5 Untuk Prediksi Loyalitas Nasabah PT Erdika Elit Jakarta,” J. Media Inform. Budidarma, vol. 4, no. 1, p. 65, 2020, doi: 10.30865/mib.v4i1.1652.
I. H. Witten, E. Frank, and M. a. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, vol. 54, no. 2. 2011.
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2023 Melek IT : Information Technology Journal
