Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting the Number of New Students

Tanuwijaya, Padmavati Darma Putri and Tjahjadi, Jhonatan Laurensius and Riti, Yosefina Finsensia (2023) Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting the Number of New Students. JISA (Jurnal Informatika dan Sains), 6 (2). pp. 161-166. ISSN 2614-8404

	Text (ARTIKEL) 14a. Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting .pdf Download (1MB)
	Text (CEK PLAGIASI) 14b.HASIL SIMILARITY-YOSEFINA F RITI- COMPARISON OF ANN BACKPROPAGATION ALGORITHM AND RANDOM FOREST.pdf Download (2MB)
	Text (KORESPONDENSI) 14c. Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting the Number of New Students.docx.pdf Download (446kB)

Abstract

Higher education institutions are educational units located at a higher level after high school or vocational school. Catholic University Darma Cendika Surabaya (UKDC) faces challenges in managing the admission of new students due to variations in the number of prospective students applying to each department, which is also influenced by changing trends in interests and job needs in Indonesia. The use of Artificial Neural Network with Backpropagation and Random Forest Regression algorithms for comparing the prediction of new student admissions in the following year will be beneficial for the administration of Catholic University Darma Cendika Surabaya (UKDC) to gain a clearer understanding of the dynamics of admissions and to support decision making in the future development of the university. The predicted number of students joining Catholic University Darma Cendika Surabaya (UKDC) in the 2024 period using Artificial Neural Network is 219 students with a Mean Squared Error (MSE) of 0,1046 and Root Mean Square Error (RMSE) of 0,32.

Item Type:	Article
Uncontrolled Keywords:	Artificial Neural Network, Backpropagation, Random Forest Regression , MSE, RMSE
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Prodi Ilmu Informatika
Depositing User:	Yosefina Finsensia Riti
Date Deposited:	10 Mar 2025 03:31
Last Modified:	10 Mar 2025 03:31
URI:	http://repositori.ukdc.ac.id/id/eprint/2178

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