ANALISIS DATA KUNJUNGAN WISATAWAN MANCANEGARAKE NTT DENGAN METODE PREDIKSI TIME SERIES
Keywords:
foreign tourists visiting data, time series prediction, autoregressive, moving averages, MAPEAbstract
One of the regional development priorities is to develop NTT as one of the gates and centers of national tourism development (Ring of Beauty). Based on BPS data seen an increase in tourists, especially foreign tourist arrivals in the last 3 years namely in 2017 the number of visits 93,455 up 29.91% from 2016., in 2018 the number of visits was 128,241, up 27.13% from 2017. The focus of local government policy NTT, which places tourism as the prime mover (prime mover), provides a positive trend for the tourism climate which leads to improving people's welfare. This is certainly supported by academics both in the field of tourism and competencies related to data analysis and forecasting. In this research approach a comparative analysis of predictive performance is performed, namely reliability (robust) and accuracy of several prediction models such as exponential smoothing (ES), trend analysis Autoregressive (AR) Moving Averages (MA), and variants of ARMA and ARIMA based on time data series of foreign tourist visits. The amount of performance is done by calculating the robustness value of the Root Mean Square Error (RMSE) and accuracy value with the value of Mean Average Percentage Error (MAPE). The results show that time series data patterns tend to be seasonal patterns rather than trend or exponential data patterns. This is indicated by the predictive performance level of Simple MA (SMA) and Weight MA (WMA), better than Exponential Smoothing (ES) and AutoRegressive (AR). The calculations show WMA Lag 3 is more reliable and accurate than SMA, with more RMSE results better 19.36% and MAPE better 23.27%. In addition, WMA Lag 3 is better than AR (1), where RMSE is 2.5% better and MAPE is 74.80% better. In the exponential pattern analysis, it is seen that ES is not good compared to WMA, where RMSE WMA is 23.52% and MAPE is 78.20% better than ES.
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