Conducted by NwSSU
, Started on 2023 -
Completed on 2023
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Higher education institutions usually do enrollment forecasting to predict the number of students in the future academic year. It is essential process that plays a crucial role in the effective planning, resource allocation and decision-making activities within the university. In this study, the enrollment was forecasted using Holt's double exponential smoothing method with the application of particle swarm optimization. The enrollment data was collected from the historical data of the university registrar. the data set involved ten years of enrollment data, where six years were allowed for the training set and the remaining four years were used for the test set. Pre-testing of the data showed the linear trend and applicability of the model. The training set was used to compute for the smoothing method where the process was optimized using the particle swarm optimization (PSO). the test set was then used to measure the performance of the model. Generally, the model performed well on making forecast and was able to correctly detect rise and fall of the enrollment data.