FORECASTING THE NUMBER OF APPLICANTS FOR GRADUATE LEVEL: A CASE STUDY OF KING MONGKUT’S UNIVERSITY OF TECHNOGY NORTH BANGKOK
Article Sidebar
Main Article Content
Abstract
The objectives of this study were to create the most suitable forecasting model for the number of applicants for graduate study a case study King Mongkut’s University of Technology North Bangkok using the Box-Jenkins approach and to indicate the forecasting value of applicants for graduate level. There were 38 samples used for this data. The applicants were classified into doctoral degree level and master’s degree level in they were divided admissions of both the first semester and the second semester of academic year 2012-2021. The Box-Jenkins approach was used as the research instrument. The results revealed that the suitable model for forecasting master’s degree students is ARIMA (1,1,4), while the suitable model for forecasting doctoral students is ARIMA (0,1,4). The result found that the number of applicants in the academic year 2022 were 589 people for master’s degree level and 71 people for doctoral degree level.
Downloads
Article Details
Copyright Notice
The copyright of research articles published in the VRU Research and Development Journal Science and Technology Journal belongs to the Research and Development Institute, Valaya Alongkorn Rajabhat University under the Royal Patronage. Reproduction of the content, in whole or in part, is prohibited without prior written permission from the university.
Responsibility
The content published in the VRU Research and Development Journal Science and Technology Journal is the sole responsibility of the author(s). The journal does not assume responsibility for errors arising from the printing process.
References
สำนักงานคณะกรรมการการอุดมศึกษา. (2551). กรอบแผนอุดมศึกษาระยะยาว 15 ปี ฉบับที่ 2 (พ.ศ.2551-2565). กรุงเทพฯ: กระทรวงศึกษาธิการ.
สำนักมาตรฐานและประเมินผลอุดมศึกษา (2560). เกณฑ์มาตรฐานหลักสูตรระดับอุดมศึกษา พ.ศ. 2558.กรุงเทพฯ: กระทรวงศึกษาธิการ.
Aditya Satrio, Christophorus Beneditto, William Darmawan, Bellatasya Unrica Nadia, and Novita Hanafiah. (2021). Time Series Analysis and Forecasting of Coronavirus Disease in Indonesia Using ARIMA Model and PROPHET. Procedia Computer Science, 179,524–32.
Al-Gounmeein, Remal Shaher, and Mohd Tahir Ismail. (2020). Forecasting the Exchange Rate of the Jordanian Dinar versus the US Dollar Using a Box-Jenkins Seasonal ARIMA Model. International Journal of Mathematics and Computer Science 15(1),27–40.
Alzahrani, Saleh I., Ibrahim A. Aljamaan, and Ebrahim A. Al-Fakih. (2020). Forecasting the Spread of the COVID-19 Pandemic in Saudi Arabia Using ARIMA Prediction Model under Current Public Health Interventions. Journal of Infection and Public Health, 13(7),914–19.
Mohamed, Jama. (2020). Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A Comparison of ARIMA and Regression with ARIMA Errors. American Journal of Theoretical and Applied Statistics, 9(4),143.
Schaffer, Andrea L., Timothy A. Dobbins, and Sallie-Anne Pearson. 2021. Interrupted Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models: A Guide for Evaluating Large-Scale Health Interventions. BMC Medical Research Methodology, 21(1),58.
