Analisis Prediksi Mahasiswa Terhadap Kelulusan Tepat Waktu Menggunakan Metode Data Mining Decision Tree (Studi Kasus: FTI UKSW)
Main Article Content
Abstract
In general, students have the responsibility to complete their studies at a university. For students of the Satya Wacana Christian University Faculty of Information Technology, which every year there are more and more students, the world of work is currently required to become someone who masters the field of technology. In addition, as a student, there are many things that must be done to complete studies by participating in activities on campus, organizations, and being active in the teaching and learning process so that they can complete their studies on time. In this study, a predictive analysis of SWCU FTI students will be conducted on timely graduation using the decision tree data mining method. which will see students who graduate on time and graduate late using the decision tree algorithm which is a decision tree algorithm that has a high level of accuracy in large amounts of data. In this study, the decision tree algorithm was used to run 983 sample data, resulting in a match accuracy of 91.25%. This means that it is very good and effective in predicting student graduation
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTIK journal and Research Division, KITA Institute as the publisher of the journal. Copyright encompasses rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
JTIK journal and Research Division, KITA Institute and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTIK journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTIK]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Muhammad Wali (Editor-in-Chief)
Editorial Office of Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Research Division, KITA Institute
Teuku Nyak Arief Street Nomor : 7b, Lamnyong, Lamgugop, Kota Banda Aceh
Telp./Fax: 0651-8070141
Email: jtik@lembagakita.org - journal@lembagakita.org
References
Mashlahah, S., 2013. Prediksi kelulusan mahasiswa menggunakan metode decision tree dengan penerapan algoritma C4. 5 (Doctoral dissertation, Universitas Islam Negeri Maulana Malik Ibrahim).
Thaniket, R., Kusrini, K. and Luthf, E.T., 2020. Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Algoritma Support Vector Machine. Jurnal FATEKSA: Jurnal Teknologi dan Rekayasa, 5(2), pp.20-29.
Rohman, A., 2015. Model Algoritma K-Nearest Neighbor (K-Nn) Untuk Prediksi Kelulusan Mahasiswa. Neo Teknika, 1(1).
Setiyani, L., Wahidin, M., Awaludin, D. and Purwani, S., 2020. Analisis Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Data Mining Naïve Bayes: Systematic Review. Faktor Exacta, 13(1), pp.35-43.
Kadhim, Q.K., 2017. Classification of human skin diseases using data mining. International Journal of Advanced Engineering Research and Science, 4(1), p.237008.
Van Truong, N., Thai, D.T.M. and Le Bich Lien, N.T.T., 2017. Improving Performance Benchmark of Decision Tree Classifications for E-mail Spam Filtering. seed, 1(100), p.20.
Bisri, A. and Wahono, R.S., 2015. Penerapan Adaboost untuk penyelesaian ketidakseimbangan kelas pada Penentuan kelulusan mahasiswa dengan metode Decision Tree. Journal of Intelligent Systems, 1(1), pp.27-32.
Kristanto, O., 2014. Penerapan algoritma klasifikasi data mining ID3 untuk menentukan penjurusan siswa SMAN 6 Semarang. Universitas Dian Nuswantoro, Semarang.
Tyasti, A.E., Ispriyanti, D. and Hoyyi, A., 2015. Algoritma Iterative Dichotomiser 3 (Id3) Untuk Mengidentifikasi Data Rekam Medis (Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta). Jurnal Gaussian, 4(2), pp.237-246.
Wang, Y. and Priestley, J.L., 2017. Binary classification on past due of service accounts using logistic regression and decision tree. Grey Lit. from PhD Candidates, vol. 4, 2017.
Romadhona, A., Suprapedi, S. and Himawan, H., 2017. Prediksi Kelulusan Mahasiswa Tepat Waktu Berdasarkan Usia, Jenis Kelamin, Dan Indeks Prestasi Menggunakan Algoritma Decision Tree. Jurnal Cyberku, 13(1), pp.8-8.
Salmu, S. and Solichin, A., 2017, April. Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu Menggunakan Naïve Bayes: Studi Kasus UIN Syarif Hidayatullah Jakarta. In Prosiding Seminar Nasional Multidisiplin Ilmu (Vol. 22).
Bukhori, A. and Pratiwi, N., 2018. Implementasi Metode Decision Tree dengan Algoritma ID3 dan C4. 5 untuk Mengklasifikasikan Partisipasi Perempuan Nikah dalam Kegiatan Ekonomi Rumah Tangga di DIY. Jurnal Statistika Industri dan Komputasi, 3(02), pp.22-32.
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S. and Zhou, Z.H., 2008. Top 10 algorithms in data mining. Knowledge and information systems, 14, pp.1-37.
Maimon, O.Z. and Rokach, L., 2014. Data mining with decision trees: theory and applications (Vol. 81). World scientific.
Andie, A., 2016. Penerapan Decision Tree Untuk Menganalisis Kemungkinan Pengunduran Diri Calon Mahasiswa Baru. Technologia: Jurnal Ilmiah, 7(1).