A comparison study on fuzzy time series and holt-winter model in forecasting tourist arrival in Langkawi, Kedah / Nur Fatihah Fauzi ... [et al.]

• Main Authors: Fauzi, Nur Fatihah, Ahmadi, Nurul Shahiera, Shafii, Nor Hayati, Ab. Halim, Huda Zuhrah
• Format: Article
• Language: English
• Published: UiTM Cawangan Perlis 2020
• Subjects: Travel and the state. Tourism; Evolutionary programming (Computer science). Genetic algorithms; Fuzzy logic
• Online Access: http://ir.uitm.edu.my/id/eprint/43553/1/43553.pdf; http://ir.uitm.edu.my/id/eprint/43553/; https://crinn.conferencehunter.com/
• Institution: Universiti Teknologi Mara

The tourism industry in Malaysia has been growing significantly over the years. Tourism has been one of the major donors to Malaysia’s economy. Based on the report from the Department of Statistics, a total of domestic visitors in Malaysia were recorded at about 221.3 million in 2018 with an increase of 7.7% alongside a higher record in visitor arrivals and tourism expenditure. This study aims to make a comparison between two methods, which are Fuzzy Time Series and Holt-Winter in forecasting the number of tourist arrival in Langkawi based on the monthly tourist arrival data from January 2015 to December 2019. Both models were generated using Microsoft Excel in obtaining the forecast value. The Mean Square Error (MSE) has been calculated in this study to get the best model by looking at the lowest value. The result found that Holt-Winter has the lowest value that is 713524285 compared to the Fuzzy Time Series with a value of 2625517469. Thus, the Holt-Winter model is the best method and has been used to forecast the tourist arrival for the next 2 years. The forecast value for the years 2020 and 2021 are displayed by month.