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

Padmavati Darma Tanuwijaya, Jhonatan Laurensius Tjahjadi, Yosefina Finsensia Riti

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.



Keywords


Artificial Neural Network, Backpropagation; Random Forest Regression; MSERMSE

Full Text:

PDF

References


R. N. Sedyati, “Perguruan tinggi sebagai agen pendidikan dan agen pertumbuhan ekonomi,” vol. 16, pp. 155–160, 2022, doi: 10.19184/jpe.v16i1.27957.

M. Metode, M. Carlo, K. K. Simulasi, and M. Carlo, “Simulasi prediksi jumlah mahasiswa baru universitas dehasen bengkulu menggunakan metode monte carlo,” vol. VII, 2020, doi:10.33369/pseudocode.7.1.8-16.

A. R. Suleman and I. Palupi, “Penerapan Artificial Neural Network ( ANN ) untuk Prediksi Prestasi Akhir Mahasiswa Melalui Nilai Mata Kuliah Dasar Tingkat 1,” vol. 10, no. 2, pp. 1849–1859, 2023.

R. Septiriana, A. Perwitasari, and R. Septiriana, “Jurnal Mantik Prediction of the Number of Course Participants Using Random Forest Regression Algorithm,” vol. 6, no. 3, 2022, doi:10.35335/mantik.v6i3.3175.

H. Putra and N. Ulfa, “Jurnal Nasional Teknologi dan Sistem Informasi Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation,” vol. 02, pp. 100–107, 2020, doi:10.25077/TEKNOSI.v6i2.2020.100-107.

M. E. Rosadi, D. Agustini, M. Farida, and D. D. Anjani, “Analisis Penerapan Neural Network dalam Memprediksi Produksi Bijih Nikel di Indonesia,” vol. 4, no. 1, pp. 40–50, 2022, doi:doi.org/10.30645/brahmana.v4i1.108.

M. R. M, R. D. Putri, M. R. N. R, S. Amin, and M. Akli, “Pengaplikasian Artificial Neural Network ( ANN ) dalam Memprediksi Curah Hujan Menggunakan Python,” pp. 369–373.

I. I. Ridho, C. F. Ramadhani, and A. P. Windarto, “Penerapan Artificial Neural Network dengan Metode Backpropagation Dalam Memprediksi Harga Saham ( Kasus : PT . Bank BCA , Tbk ) diantaranya oleh A . Santoso dan S . Hansun [ 1 ] dalam riset mereka yang sebenarnya . Dalam kesimpulannya , penerapan NN dalam memprediksi harga,” vol. 8, pp. 295–303, 2023.

E. Fitri, “JOURNAL OF APPLIED COMPUTER SCIENCE AND TECHNOLOGY ( JACOST ) Analisis Perbandingan Metode Regresi Linier , Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah,” vol. 4, no. 1, pp. 58–64, 2023, doi:10.52158/jacost.v4i1.491.

K. Ciptady, M. Harahap, and Y. Ndruru, “Prediksi Kualitas Kopi Dengan Algoritma Random Forest Melalui Pendekatan Data Science,” vol. 2, no. 1, 2022, doi:10.47709/dsi.v2i1.1708.

S. Kasus, S. Ciliwung, C. River, and C. Study, “Jurnal Teknologi Lingkungan Prediksi Kualitas Air Sungai Menggunakan Metode Pembelajaran Mesin : Studi Kasus Sungai Ciliwung Prediction of River Water Quality Using Machine Learning Methods : Ciliwung River Case Study,” vol. 24, no. 2, pp. 273–282, 2023.

C. Science, V. W. Siburian, and I. E. Mulyana, “Prediksi Harga Ponsel Menggunakan Metode Random Forest,” vol. 4, no. 1, pp. 978–979, 2018.

N. A. Riani, R. Andreswari, R. Fauzi, and S. Informasi, “IMPLEMENTASI ALGORITMA ARTIFICIAL NEURAL NETWORK,” vol. 4307, no. 3, pp. 241–247, 2021.

M. Adi, P. Hutabarat, M. Julham, A. Wanto, P. Algoritma, and M. Produksi, “Meychael Adi Putra Hutabarat* 1 , Muhammad Julham 2 , Anjar Wanto 3,” vol. 4, no. 1, pp. 77–86, 2018.

J. R. Saragih, M. Billy, S. Saragih, and A. Wanto, “ANALISIS ALGORITMA BACKPROPAGATION DALAM PREDIKSI NILAI EKSPOR ( JUTA USD ),” vol. 15, no. 2, pp. 254–264, 2018, doi:10.23887/jptk-undiksha.v15i2.14362.

R. H. Dananjaya, “PENERAPAN ARTIFICIAL NEURAL NETWORK ( ANN ) DALAM MEMPREDIKSI,” vol. 10, no. 4, pp. 419–426, 2022, doi:10.20961/mateksi.v10i4.65034.

R. Sistem, D. Kartini, F. Abadi, and T. H. Saragih, “Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural,” vol. 1, no. 10, pp. 39–44, 2021, doi:10.29207/resti.v5i1.2602.

G. I. Marthasari, S. A. Astiti, and Y. Azhar, “Prediksi Data Time-series menggunakan Jaringan Syaraf Tiruan Algoritma Backpropagation Pada Kasus Prediksi Permintaan Beras,” vol. 6, no. 3, pp. 187–193, 2021, doi:https://dx.doi.org/10.30591/jpit.v6i3.2627.

C. E. Larsen, R. Trip and C. R. Johnson, “Methods for procedures related to the electrophysiology of the heart”, U.S. Patent 5,529,067, (1995) June 25.




DOI: https://doi.org/10.31326/jisa.v6i2.1789

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Padmavati Darma Tanuwijaya, Jhonatan Laurensius Tjahjadi, Yosefina Finsensia Riti

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOURNAL IDENTITY

Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December 
Language: English
APC: The Journal Charges Fees for Publishing 
Indexing:  EBSCO , DOAJ, Google Scholar, Arsip Relawan Jurnal Indonesia, Directory of Research Journals Indexing, Index Copernicus International, PKP Index, Science and Technology Index (SINTA, S4) , Garuda Index
OAI address: http://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contact: jisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi

In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal Indonesia, Jurnal Teknologi dan Sistem Komputer (JTSiskom)

 

 

JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.