Abstract
This research investigates the factors that influence students' decisions to withdraw from campus with the aim of developing strategies to increase student participation through the application of the decision tree method. Decision Trees are used to form decision trees that are easy to interpret and enable statistical pattern recognition. Research is based on UKT group criteria, social studies and GPA scores, student semesters, and credits taken. Research data was taken through a literature study from the Kaggle web platform regarding the number of students who withdrew. The research results show that the highest rate of resignation occurs among students who are not active in organizations. Even though students with a GPA in the range of 3.00-3.78 experienced withdrawal, the findings confirmed that GPA was not the main reason. The smallest UKT group (2-3) shows a lower withdrawal rate than the largest UKT group. This confirms that students who are less active in organizations are the students who resign the most. As a strategy to increase student participation, research recommends that students increase awareness and promote organizational activities, lecturers can create coaching and guidance programs, academic support. This strategy can help create a campus environment that supports student participation in organizational activities.
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W. Wulandari, R. Rosnelly, W. Wanayumini, Analisis Metode Decision Tree Dalam Memprediksi Kelulusan Mahasiswa, CSRID (Computer Sci. Res. Its Dev. Journal). 13 (2021) 131. https://doi.org/10.22303/csrid.13.3.2021.131-140.
A.H. Nasrullah, Implementasi Algoritma Decision Tree Untuk Klasifikasi Produk Laris, J. Ilm. Ilmu Komput. 7 (2021) 45–51. https://doi.org/10.35329/jiik.v7i2.203.
Marnis & Priyono, Manajemen Sumber Daya Manusia, 2008. https://doi.org/10.1017/CBO9781107415324.004.
S.M.K. Swasta, A. Bangsa, BEES : Bulletin of Electrical and Electronics Engineering Implementasi Metode Decision Tree Pada Tingkat Prestasi Belajar, 1 (2021) 108–114.
B. Miftahurrohmah, C. Wulandari, Analisis Prediksi Mahasiswa Mengundurkan Diri Dari Universitas XYZ Dengan Metode Support Vector Machine, Netw. Eng. Res. Oper. 4 (2019) 173–179.
R. Fauzi, Klasifikasi Siswa Yang Akan Mengikuti Lomba Olimpiade Sains Nasional (OSN) Menggunakan Algoritma C4.5 (Studi Kasus: SMP Negeri 2 Gresik), Undergrad. Thesis, Univ. Muhammadiyah Gresik. (2019) 6–17. http://eprints.umg.ac.id/2109/.
S. Jusuf, Pengantar Metodologi Penelitian, 2012.
Normah, B. Rifai, S. Vambudi, R. Maulana, Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE, J. Tek. Komput. AMIK BSI. 8 (2022) 174–180. https://doi.org/10.31294/jtk.v4i2.
Ms. Prof. Dr. Suryana, Metodologi Penelitian : Metodologi Penelitian Model Prakatis Penelitian Kuantitatif dan Kualitatif, Univ. Pendidik. Indones. (2012) 1–243. https://doi.org/10.1007/s13398-014-0173-7.2.
E.D. Kartiningrum, Panduan Penyusunan Studi Literatur, Lemb. Penelit. Dan Pengabdi. Masy. Politek. Kesehat. Majapahit, Mojokerto. (2015) 1–9.
N.E. Helwig, S. Hong, E.T. Hsiao-wecksler, data mahasiswa (kaggle), (n.d.).
N.P.A. Widiari, I.M.A.D. Suarjaya, D.P. Githa, Teknik Pengolahan Data Cleaning, J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi). 8 (2020) 137.
B.A.B. Ii, A.P. Terdahulu, BAB II TINJAUAN PUSTAKA A. Penelitian Terdahulu 1., (2006) 9–37.
I.S. Wijaya, M. Wijaya, Sistem Pendukung Keputusan Untuk Menentukan Jurusan Pada Beasiswa Pada Sma Negeri 11 Muaro Jambi, J. Process. 18 (2023) 40–53. https://ejournal.unama.ac.id/index.php/processor/article/view/155.
S. Bahri, A. Lubis, Metode Klasifikasi Decision Tree Untuk Memprediksi Juara English Premier League, J. Sintaksis. 2 (2020) 63–70.
L.R. Haidar, E. Sediyono, A. Iriani, ANALISA PREDIKSI MAHASISWA DROP OUT MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA ID3 dan C4.5, J. Transform. 17 (2020) 97. https://doi.org/10.26623/transformatika.v17i2.1609.
M.N.M. Rahayu, R. Arianti, Penyesuaian Mahasiswa Tahun Pertama Di Perguruan Tinggi: Studi Pada Mahasiswa Fakultas Psikologi Uksw, J. Psychol. Sci. Prof. 4 (2020) 73. https://doi.org/10.24198/jpsp.v4i2.26681.
Aprilla Dennis, Belajar Data Mining dengan RapidMiner, Innov. Knowl. Manag. Bus. Glob. Theory Pract. Vols 1 2. 5 (2013) 1–5. http://esjournals.org/journaloftechnology/archive/vol1no6/vol1no6_6.pdf%5Cnhttp://www.airccse.org/journal/nsa/5413nsa02.pdf.
I.S. Melati, L. Linawati, I.A.. Giriantari, Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa, Maj. Ilm. Teknol. Elektro. 17 (2018) 325. https://doi.org/10.24843/mite.2018.v17i03.p04.
D. Manurung, F. Sandi, F. Akinardipura, H. AShfahan, D. Prasvirta, Prediksi Pengunduran Diri Karyawan Perusahaan “Y” Menggunakan, Semin. Nas. Mhs. Imu Komput. Dan Apl. 2 (2021) 202–213.
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