Time series data analysis to forecast the percentage of on-time graduates at Nusa Cendana university

Ch. Krisnandari Ekowati (), Siprianus Suban Garak (), Aleksius Madu (), Imelda Hendriani Eku Rimo () and Fransiska Atrik Halim ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 260-278

Abstract: The existence of information related to the estimated percentage of students graduating on time can certainly be one of the study materials that stakeholders can use to take strategies to increase the percentage of on-time graduates. One way that can be done is to use time series data related to the percentage of on-time graduates in the past year to predict the percentage of on-time graduates in the future. Forecasting can be done using ARIMA modeling. In addition, so that the solution used is also right on target, it is necessary to find out the factors that affect the study period of Undana students. The type of research used is mixed method research with sequential explanatory method where, in the first stage of research using quantitative methods, namely collecting data and quantitative analysis and in the second stage collecting and analyzing qualitative data. The quantitative data used is the percentage of on-time graduates at Nusa Cendana University in the last 5 years, while the qualitative data is the results of interviews with graduates of Nusa Cendana University related to factors that affect the length of study. The results showed that the best model for forecasting the percentage of on-time graduates of undergraduate students at Nusa Cendana University is the ARIMA (2, 2, 1) model with an AIC value of -125.64. The results of forecasting the percentage of on-time for the February, June, and September graduation periods in the next 5 years, namely 2024 to 2028, are 5.31%, 9.84%, 12.79%, 9.05%, 13.08%, 15.91%, 12.74%, 16.35%, 19.06%, 16.39%, 19.63%, 22.22%, 20%, 22.93%, and 25.41% respectively. Meanwhile, the factors that influence the study period of students are the curriculum applied in the study program, facilities and infrastructure, lecturers, and students themselves.

Keywords: Academic performance; Arima; Data forecasting; Time series analysis. (search for similar items in EconPapers)
Date: 2024
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