Open AccessAugmentation time series model with Kalman filter to predict foreign tourist arrivals in East Java
Author(s) -
Evita Purnaningrum,
Sari Cahyaningtias,
D A Kusumawardhani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1869/1/012116
Subject(s) - kalman filter , tourism , computer science , java , filter (signal processing) , extended kalman filter , promotion (chess) , time series , operations research , marketing , business , artificial intelligence , engineering , geography , machine learning , political science , politics , archaeology , law , computer vision , programming language
One of the focuses of Indonesia’s development is on the tourism sector. In addition, one of the supporters of the sector’s sustainability is the customer. So, customer satisfaction who comes and their desire to come back again, as well as an indirect promotion to other potential visitors. The availability and convenience of infrastructure and facilities are important to support this. In other words, the prediction of visiting foreign tourists is one form of alertness in preparing future tourism projections. However, the fluctuation of data for foreign tourists affects the effectiveness of the model in making predictions. Kalman filter is a stochastic deterministic model that can solve this problem. This study combines the time series model with the Kalman filter to determine the prediction of the number of foreign tourists visiting East Java. The results of these predictions can be concluded that the Kalman filter is able to handle fluctuating data with RMSE close to 0. The prediction for this paper could be help enterprise to decide future plan for give the tourist discount.