International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 4, 8 Aug. 2026
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Artificial Neural Network-Based Time Series Forecasting for Higher Education Enrollment: A Case Study of NEMSU–Cantilan Campus
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Author(s)
Index Terms
Student Enrollment Forecasting, Machine Learning, Artificial Neural Networks, BSCS, BSCpE, BSIT, Higher Education Planning, Data-Driven Decision Making
Abstract
This paper presents a machine learning-based model designed to forecast trends in enrollment in the Department of Computer Studies at NEMSU–Cantilan Campus, specifically for the Bachelor of Science in Computer Science (BSCS), Bachelor of Science in Computer Engineering (BSCpE), and Bachelor of Science in Information Technology (BSIT) programs. Twenty semesters (2015–2025) of historical program-level enrollment were chronologically split, with the most recent semesters withheld for validation, and a lagged feature set (Lag 1–Lag 3) was constructed for an Artificial Neural Network (ANN) and benchmarked against Holt-Winters Exponential Smoothing and ARIMA, both tuned on the same training split. Employing the ANN model, this study achieved an aggregate Mean Absolute Percentage Error (MAPE) of 3.62%, outperforming the Holt-Winters (25.59%) and ARIMA (31.09%) baselines on this dataset, a ranking corroborated by RMSE and MAE (Table 6) and confirmed against a small LSTM and Prophet, neither of which outperformed the ANN at this sample size; a per-program breakdown (Table 7), however, shows this advantage is not uniform, ranging from 3.93% MAPE for BSCS to 13.48% for BSIT. The enrollment projections indicate consistent growth across all programs. The BSCS program is likely to increase from 297 students in 2025 to 401 by 2028, exhibiting a 35% growth. The BSIT program is projected to experience the most significant expansion, growing from 1,078 students in 2025 to 2,714 students by 2028-an increase of 151%, a trend consistent with the program’s sharp historical acceleration after 2022 and the recursive nature of the multi-step forecast, which compounds this recent growth forward. Meanwhile, the BSCpE program is expected to grow more gradually, from 153 students in 2025 to 186 by 2028, showing a 21.6% increase. Overall, the total enrollment in the Department of Computer Studies is expected to rise from 1,517 students in the second semester of 2025 to 3,031 by the first semester of 2028, marking a 100% increase. These findings highlight the accuracy and adaptability of ANN-based models in capturing nonlinear trends in enrollment, offering a valuable tool for strategic planning, faculty and classroom allocation in state universities and colleges in the Philippines.
Cite This Paper
Ariel A. Dormendo, Esmael V. Maliberan, "A Artificial Neural Network-Based Time Series Forecasting for Higher Education Enrollment: A Case Study of NEMSU–Cantilan Campus", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.4, pp. 16-30, 2026. DOI:10.5815/ijwmt.2026.04.02
Reference
[1]B. Ayasi, M. Saleh, Á. García-Vico, and C. Carmona, “Predicting course enrollment with machine learning and neural networks: A comparative study of algorithms,” in Studies on Social and Education Sciences 2023, ISTES Organization, 2023, pp. 157–182.
[2]W. D. Ayasi, “Predicting course enrollment with machine learning,” 2024. [Online]. Available: https://www.aaup.edu/about-university/faculty-members/bahgat-waleed-deeb-ayasi/publications/predicting-course-enrollment
[3]A. J. Patungan and M. L. M. Francia, “A machine learning modeling prediction of enrollment among admitted college applicants at University of Santo Tomas,” AIP Conf. Proc., vol. 2560, no. 1, 2022. https://doi.org/10.1063/5.0100174
[4]R. H. Albornoz-Toyohama, S. H. Haro-Casildo, C. D. Abanto-Ramírez, and R. Mendigure-Hachircana, “Factors influencing student retention university students: an analysis of institutional reputation, social responsibility and loyalty,” *Front. Educ.*, vol. 10, 2025. [Online]. Available: https://doi.org/10.3389/feduc.2025.1499518
[5]A. Rushiti et al., “The Republic of North Macedonia's Research Ranking Platform for Academic Staff and Universities,” SAR Journal, vol. 7, no. 1, pp. 3–11, 2024. https://doi.org/10.18421/SAR71-01
[6]F. E. James and J. L. Weese, “Neural network-based forecasting of student enrollment with exponential smoothing baseline and performance analysis,” in ASEE Annu. Conf. & Expo., Minneapolis, MN, USA, 2022. https://doi.org/10.18260/1-2--41751
[7]W. Chen, Y. Zhou, and Y. Wang, “A hybrid neural network model for tourist arrivals forecasting,” J. Travel Res., vol. 59, no. 7, pp. 1199–1215, 2021. https://doi.org/10.1177/0047287520919522
[8]R. Ramos, “Machine learning-based enrollment prediction for a higher education institution,” Asia Pac. J. Manage. Sustain. Dev., vol. 12, no. 2, 2024.
[9]J. A. Esquivel and J. A. Esquivel, “A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University,” International Journal of Computer Trends and Technology, vol. 69, no. 5, pp. 50–54, 2021.
[10]L. Shao, M. Ieong, R. A. Levine, J. Stronach, and J. Fan, “Machine Learning Methods for Course Enrollment Prediction,” Strategic Enrollment Management Quarterly, vol. 10, no. 2, pp. 68–85, 2022. [Online]. Available: https://par.nsf.gov/servlets/purl/10389427
[11]Y. Hu, L. Yan, T. Hang, and J. Feng, “Stream-flow forecasting of small rivers based on LSTM,” arXiv preprint arXiv:2001.05681, 2020. https://doi.org/10.48550/arXiv.2001.05681
[12]S. Jain, P. Kumar, and A. Shukla, “A novel ensemble ARIMA–LSTM approach for evaluating nonlinear time series forecasting,” Front. Appl. Math. Stat., 2024. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11671211/
[13]M. A. Z. Khan, “Estimate undergraduate student enrollment in courses by re-purposing recommendation tools,” ResearchGate Preprint, 2024. [Online]. Available: https://www.researchgate.net/publication/385175624_Estimate_Undergraduate_Student_Enrollment_in_Courses_by_Repurposing_Recommendation_Tools
[14]M. Görën, “Early prediction of student dropout using educational data mining techniques,” in EDM 2024 Proc., 2024. [Online]. Available: https://educationaldatamining.org/edm2024/proceedings/2024.EDM-short-papers.32/
[15]A. Turkmenbayev, A. Kapanova, and K. Asanov, “The application of machine learning in predicting student outcomes: A systematic review,” Front. Educ., vol. 10, 2025. https://doi.org/10.3389/feduc.2025.1562586
[16]H. Zhou and J. Yu, “DTDNet: Deep travel demand forecasting network for tourism demand prediction,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 3, pp. 1001–1013, 2021. https://doi.org/10.1109/TNNLS.2021.3078705
[17]R. V. Maliberan, “Forecasting Tourist Arrival in the Province of Surigao del Sur, Philippines Using a Time Series Analysis,” International Journal on Informatics Visualization. vol. 3, no. 3, pp. 255–261, 2019. doi10.30630/joiv.3.3.268
[18]G. M. Masinading, S. E. Dalagan Jr., and S. K. R. Manib, “Forecasting the semestral enrollment of DOrSU curricular programs,” Adv. Appl. Stat., vol. 91, no. 12, pp. 1579–1592, 2024. https://doi.org/10.17654/0972361724080
[19]F. E. De Guzman, “Admission prediction for freshmen students using low-cost data analytics (Naïve Bayes),” J. Data Anal., vol. 2, no. 1, pp. 55–63, 2024. [Online]. Available: https://journal-data.ir/index.php/JDA/article/view/32
[20]J. Libo-on and J. Gadon, “Forecasting enrollment: A substitute to career guidance campaign,” Romblon State Univ. Res. J., vol. 5, no. 2, pp. 22–31, 2021. [Online]. Available: https://ojs.rsu.edu.ph/index.php/rsurj/article/view/58
[21]S. F. Ríos-Parra, “Predictive Analysis of Student Enrollment in Higher Education Using Random Forest,” Universidad de los Andes, 2024. [1][Online]. Available: https://repositorio.uniandes.edu.co/bitstreams/baf52aa1-6101-4f70-aef0-59da3923ba51/download.
[22]M. S. Loren, M. A. Miranda, and C. K. A. Correos, “Evaluation of the student affairs and services: Service enhancement,” Sci. Int. (Lahore), vol. 35, no. 1, pp. 61–66, 2023. [Online]. Available: https://www.sci-int.com/pdf/638465283486592359.pdf
[23]A. P. Dela Cruz, R. P. Medina, and A. Sison, “Higher Education Institution (HEI) Enrollment Forecasting Using Data Mining Technique,” in 2020 8th International Conference on Information and Education Technology (ICIET), 2020.
[24]E. Alshdaifat, M. Alshraideh, F. Al-Maatouk, and A. Al-Sayyed, “The impact of data normalization on predicting student performance: A case study from Hashemite University,” Int. J. Adv. Comput. Sci. Appl. (IJACSA), vol. 11, no. 9, pp. 87–93, 2020.
[25]M. Görmez and S. Aydin, “Multilayer perceptron-based short-term forecasting for small-sample institutional time series,” Neural Comput. Appl., vol. 36, pp. 4211–4224, 2024. https://doi.org/10.1007/s00521-023-09142-7
[26]R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, 2006. https://doi.org/10.1016/j.ijforecast.2006.03.001
[27]Q. Chen, “A Comparative Study on the Forecast Models of the Enrollment Proportion of General Education and Vocational Education,” International Education Studies, vol. 15, no. 6, pp. 109–123, 2022. [Online]. Available: https://files.eric.ed.gov/fulltext/EJ1373321.pdf