Penerapan Adaboost Berbasis Pohon Keputusan Guna Menentukan Pola Masuknya Calon Mahasiswa Baru
DOI:
https://doi.org/10.26623/transformatika.v18i1.1606Keywords:
higher education, decision tree, adaptive boostingAbstract
In general, the college admission process is done through registration, file selection, examinations, an announcement of the results of students who pass, and ends with re-registration. In this case, a problem was found where there is a significant decrease in the number of student who register with those who re-register .Things like this can reduce the balance between new students and students who meet the requirements, to make a decrease in the quality of higher education and affect accreditation. Based on these problems, a classification method was developed to look for patterns of students who would enter institutions and what factors influence students to re-register.
To improve the accuracy of the decision tree algorithm the author use adaptive boosting (adaboost) in finding factors that make prospective students continue to the re-registration process.
From the results of the study, the AdaBoost-based decision tree algorithm shows that the level of accuracy has an increase of 20%. The presentation of results is as follows, 61.4% (decision tree); 91.35% (decision tree + AdaBoost)References
R. A. Kaufman, Educational system planning. Englewood Cliffs : Prentice-Hall, 1972.
M. Somantri, PERENCANAAN PENDIDIKAN. IPB Press, 2014.
Y. H. Maturbongs and R. Satria, PERENCANAAN STRATEGIS SISTEM INFORMASI PADA INSTITUSI PENDIDIKAN TINGGI, vol. 2011, no. Snati, pp. 17 18, 2011.
N. A. Ismail, A Case Analysis of Information Technology Management in a Public, pp. 505 511, 2020.
M. A. Al Karomi, OPTIMASI PARAMETER K PADA ALGORITMA KNN UNTUK KLASIFIKASI HEREGISTRASI MAHASISWA Program Studi Teknik Informatika STMIK Widya Pratama Jl . Patriot 25 Pekalongan email : adib.comp@gmail.com, no. 1, pp. 28 33, 2015.
D. T. Larose and C. D. Larose, An Introduction to Data Mining, 2014.
J. Han and M. Kamber, Data Mining: Concepts and Techniques, Second. San Francisco: Morgan Kaufmann.
M. D. Borah, R. Jindal, D. Gupta, and G. C. Deka, Application of knowledge based decision technique to Predict student enrollment decision, pp. 180 184, 2011.
P. Pumpuang, Comparisons of Classifier Algorithms : Bayesian Network , C4 . 5 , Decision Forest and NBTree for Course Registration Planning Model of Undergraduate Students, pp. 3647 3651, 2008.
D. Aribowo, A. Ekyanto, and H. Setiadi, Analisa Komparasi Algoritma Data Mining untuk Klasifikasi Heregistrasi Calon Mahasiswa STMIK Widya Pratama, vol. XIII, no. 2, pp. 1 6, 2018.
F. A. Hermawati, Data Mining, Yogyakarta: Penerbit Andi, 2013.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Transformatika is licensed under a Creative Commons Attribution 4.0 International License.
