Abstract

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

Highlights

  • The result of this study indicates that the time series regression model is a better model

  • Diakses 8 Maret 2019, 16:48 WIB. https://visitingjogja.com

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Summary

Metode Penelitian

Dengan data in-sample sebanyak 60 observasi dari bulan Januari 2012 hingga Desember. 1. 2. Menentukan variabel dummy untuk efek variasi kalender hari Raya Idul Fitri dan musiman 12 bulan. Variabel dummy untuk variasi kalender yaitu bulan yang terdapat hari raya Idul Fitri adalah Vt, yang bernilai 1 untuk bulan yang terdapat hari raya sedangkan untuk bulan lainnya bernilai 0. 3. Melakukan pemodelan regresi runtun waktu dengan variabel dummy. Estimasi parameter model regresi runtun waktu variabel dummy dilakukan dengan metode. Jika terdapat parameter variabel dummy yang tidak signifikan maka dilakukan estimasi ulang dengan tidak melibatkan parameter yang tidak signifikan tersebut sehingga akan diperoleh model regresi runtun waktu dengan semua parameter yang telah signifikan. 5. Melakukan pemodelan SARIMA dengan melihat plot ACF dan PACF pada data yang telah stasioner. 7. Membandingkan RMSE in-sample dan out-sample dari model yang residunya telah memenuhi asumsi white noise dan berdistribusi normal. Melakukan peramalan pengunjung obyek wisata pantai Pandansimo menggunakan model terbaik

Hasil dan Pembahasan
Pemodelan Regresi Runtun Waktu
Kesimpulan
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