Forecasting the number of lecturers by multi-input intervention model for human resource university planning policy

Abstract

Institut Teknologi Sepuluh Nopember (ITS) as one of the leading universities in Indonesia that is currently transforming into an internationally reputed research university, must have a plan for employee needs, in this case, lecturers who can meet the needs of the university in line with organizational change. Good Human Resource planning must be supported by complete data and information. One way to predict the needs of lecturers is to do time series modelling using data on the number of ITS lecturers in previous years. The time series modelling used is intervention modelling because the initial hypothesis of this study was the number of lecturers' needs had a significant change when ITS changed to BLU and PTNBH. Based on the background description above, the purpose of this study is to obtain an appropriate time series intervention model to predict the number of ITS lecturers based on the model that has been produced. Based on the results of data analysis, the best intervention model is ARIMA ([4, 5], 1, 0). It means that the number of lecturers in the t-month is influenced by the number of lecturers in the 4th, 5th, 6th months ago, and the forecasting results show that the number of lecturers will decrease every month, so it is necessary to add the number of lecturers regularly every year.